Rasheed Hameed

Assignment 2

Problem 3 - Census data

For this problem you will use a simplified version of the Adult Census Data Set. In the subset provided here, some of the attributes have been removed and some preprocessing has been performed.

In [90]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
In [154]:
#Read the data into panda's dataframe
df = pd.read_csv("adult-modified.csv", na_values=['?'])
df.head(10)
Out[154]:
age workclass education marital-status race sex hours-per-week income
0 39.0 Public 13 Single White Male 40 <=50K
1 50.0 Self-emp 13 Married White Male 13 <=50K
2 38.0 Private 9 Single White Male 40 <=50K
3 53.0 Private 7 Married Black Male 40 <=50K
4 28.0 Private 13 Married Black Female 40 <=50K
5 37.0 Private 14 Married White Female 40 <=50K
6 49.0 Private 5 Married Black Female 16 <=50K
7 52.0 Self-emp 9 Married White Male 45 >50K
8 31.0 Private 14 Single White Female 50 >50K
9 42.0 Private 13 Married White Male 40 >50K

a. Preprocessing and data analysis:

*Examine the data for missing values. In case of categorical attributes, remove instances with missing values. In the case of numeric attributes, impute and fill-in the missing values using the attribute mean.

*Examine the characteristics of the attributes, including relevant statistics for each attribute, histograms illustrating the distributions of numeric attributes, bar graphs showing value counts for categorical attributes, etc.

*Perform the following cross-tabulations (including generating bar charts): education+race, work-class+income, work-class+race, and race+income. In the latter case (race+income) also create a table or chart showing percentages of each race category that fall in the low-income group. Discuss your observations from this analysis.

*Compare and contrast the characteristics of the low-income and high-income categories across the different attributes.

In [155]:
df.describe(include='all')
Out[155]:
age workclass education marital-status race sex hours-per-week income
count 9802.000000 9412 10000.000000 10000 10000 10000 10000.000000 10000
unique NaN 3 NaN 2 5 2 NaN 2
top NaN Private NaN Single White Male NaN <=50K
freq NaN 6947 NaN 5017 8556 6703 NaN 7621
mean 38.449806 NaN 10.076600 NaN NaN NaN 40.530300 NaN
std 13.611949 NaN 2.548172 NaN NaN NaN 12.277197 NaN
min 17.000000 NaN 1.000000 NaN NaN NaN 1.000000 NaN
25% 27.000000 NaN 9.000000 NaN NaN NaN 40.000000 NaN
50% 37.000000 NaN 10.000000 NaN NaN NaN 40.000000 NaN
75% 47.000000 NaN 12.000000 NaN NaN NaN 45.000000 NaN
max 90.000000 NaN 16.000000 NaN NaN NaN 99.000000 NaN

from first observation of missing values we see that age, workclass, education, marital-status, race, sex, hours-per-week, and income all have missing items. Categorical variables that need to be scrubbed for missing values are workclass, marital_status, race, sex, and income

In [163]:
df.columns
Out[163]:
Index(['age', 'workclass', 'education', 'marital-status', 'race', 'sex',
       'hours-per-week', 'income'],
      dtype='object')
In [183]:
df["age"].plot(kind="hist", bins=10)
Out[183]:
<matplotlib.axes._subplots.AxesSubplot at 0x1f5a4003c50>
In [165]:
df['workclass'].value_counts().plot(kind='bar', color='red')
Out[165]:
<matplotlib.axes._subplots.AxesSubplot at 0x1f5a3d33cf8>
In [185]:
df["education"].plot(kind="hist", bins=10)
Out[185]:
<matplotlib.axes._subplots.AxesSubplot at 0x1f5a40759e8>
In [188]:
df['marital-status'].value_counts().plot(kind='bar', color='green')
Out[188]:
<matplotlib.axes._subplots.AxesSubplot at 0x1f5a5325940>
In [190]:
df['race'].value_counts().plot(kind='bar', color='orange')
Out[190]:
<matplotlib.axes._subplots.AxesSubplot at 0x1f5a3f38518>
In [192]:
df['hours-per-week'].plot(kind="hist", bins=10)
Out[192]:
<matplotlib.axes._subplots.AxesSubplot at 0x1f5a3f91d68>
In [194]:
df['income'].value_counts().plot(kind='bar', color='purple')
Out[194]:
<matplotlib.axes._subplots.AxesSubplot at 0x1f5a5234be0>
In [195]:
df.isnull().sum()
Out[195]:
age                 0
workclass         588
education           0
marital-status      0
race                0
sex                 0
hours-per-week      0
income              0
dtype: int64
In [196]:
df.shape
Out[196]:
(10000, 8)
In [117]:
df['workclass'].value_counts()
Out[117]:
Private     6947
Public      1317
Self-emp    1148
?            588
Name: workclass, dtype: int64
In [201]:
#interpolate using the mean in age
age_mean = df.age.mean()
df.age.fillna(age_mean, axis=0, inplace=True)
In [199]:
#dropping all the 588 records of na from workclass
df = df.dropna(subset=['workclass'])
In [200]:
df.shape
Out[200]:
(9412, 8)
In [202]:
df.describe()
Out[202]:
age education hours-per-week
count 9412.000000 9412.000000 9412.000000
mean 38.366342 10.125266 41.080217
std 12.962039 2.542118 11.884590
min 17.000000 1.000000 1.000000
25% 28.000000 9.000000 40.000000
50% 37.000000 10.000000 40.000000
75% 47.000000 13.000000 45.000000
max 90.000000 16.000000 99.000000

*Perform the following cross-tabulations (including generating bar charts): education+race, work-class+income, work-class+race, and race+income. In the latter case (race+income) also create a table or chart showing percentages of each race category that fall in the low-income group. Discuss your observations from this analysis.

In [207]:
gg = pd.crosstab(df["education"],df["race"])
gg
Out[207]:
race Amer-Indian Asian Black Hispanic White
education
1 0 0 1 0 11
2 0 1 4 3 38
3 0 4 5 1 71
4 5 5 14 6 150
5 0 3 19 2 118
6 8 3 30 4 223
7 4 6 49 4 261
8 0 2 17 3 78
9 35 67 350 23 2590
10 26 64 206 11 1818
11 5 10 33 4 337
12 4 5 33 3 259
13 5 75 102 8 1387
14 0 27 20 1 467
15 0 11 5 2 153
16 0 8 4 0 101
In [216]:
gg.plot(kind="bar",figsize=(10,7))
Out[216]:
<matplotlib.axes._subplots.AxesSubplot at 0x1f5a5cc3400>
In [217]:
gg = pd.crosstab(df["workclass"],df["income"])
gg
Out[217]:
income <=50K >50K
workclass
Private 5443 1504
Public 925 392
Self-emp 725 423
In [218]:
gg.plot(kind="bar",figsize=(10,7))
Out[218]:
<matplotlib.axes._subplots.AxesSubplot at 0x1f5a585e780>
In [219]:
gg = pd.crosstab(df["workclass"],df["race"])
gg
Out[219]:
race Amer-Indian Asian Black Hispanic White
workclass
Private 65 204 664 64 5950
Public 20 48 192 5 1052
Self-emp 7 39 36 6 1060
In [220]:
gg.plot(kind="bar",figsize=(10,7))
Out[220]:
<matplotlib.axes._subplots.AxesSubplot at 0x1f5a55d7128>
In [221]:
gg = pd.crosstab(df["race"],df["income"])
gg
Out[221]:
income <=50K >50K
race
Amer-Indian 83 9
Asian 224 67
Black 773 119
Hispanic 69 6
White 5944 2118
In [222]:
gg.plot(kind="bar",figsize=(10,7))
Out[222]:
<matplotlib.axes._subplots.AxesSubplot at 0x1f5a5528be0>
In [238]:
#chart showing percentages of each race category that fall in the low-income group
gg = (pd.crosstab(df["race"],df["income"]) /pd.crosstab(df["race"],df["income"]).sum())*100  
gg
Out[238]:
income <=50K >50K
race
Amer-Indian 1.170168 0.388098
Asian 3.158043 2.889176
Black 10.898069 5.131522
Hispanic 0.972790 0.258732
White 83.800930 91.332471

from the bar graph and chart it is visible that the majority of the population for this data set are whites. The white population from this data set also have the 83% make less than 50K, followed by black at 11% and the least at for hispanic 1%. For higher than 50K income indviduals belong to white race. The lowest set of individuals belong to Amer-Indian race and they fall at 0.4%.

b. Predictive Modeling and Model Evaluation:

*Using either Pandas or Scikit-learn, create dummy variables for the categorical attributes. Then separate the target attribute ("income>50K") from the attributes used for training. [Note: you need to drop "income<=50K" which is also created as a dummy variable in earlier steps).

*Use scikit-learn to build classifiers uisng Naive Bayes (Gaussian), decision tree (using "entropy" as selection criteria), and linear discriminant analysis (LDA). For each of these perform 10-fold cross-validation (using cross-validation module in scikit-learn) and report the overall average accuracy.

*For the decision tree model (generated on the full training data), generate a visualization of tree and submit it as a separate file (png, jpg, or pdf) or embed it in the Jupyter Notebook.

In [240]:
df_modified = pd.get_dummies(df)
df_modified.head()
Out[240]:
age education hours-per-week workclass_Private workclass_Public workclass_Self-emp marital-status_Married marital-status_Single race_Amer-Indian race_Asian race_Black race_Hispanic race_White sex_Female sex_Male income_<=50K income_>50K
0 39.0 13 40 0 1 0 0 1 0 0 0 0 1 0 1 1 0
1 50.0 13 13 0 0 1 1 0 0 0 0 0 1 0 1 1 0
2 38.0 9 40 1 0 0 0 1 0 0 0 0 1 0 1 1 0
3 53.0 7 40 1 0 0 1 0 0 0 1 0 0 0 1 1 0
4 28.0 13 40 1 0 0 1 0 0 0 1 0 0 1 0 1 0
In [243]:
#separate the target attribute ("income_>50K")
df_target = df_modified['income_>50K']
df_target.head(10)
Out[243]:
0    0
1    0
2    0
3    0
4    0
5    0
6    0
7    1
8    1
9    1
Name: income_>50K, dtype: uint8
In [245]:
#drop "income_<=50K" which is also created as a dummy variable in earlier steps)
df_new = df_modified.drop(['income_<=50K','income_>50K'], axis=1)
df_new.head()
Out[245]:
age education hours-per-week workclass_Private workclass_Public workclass_Self-emp marital-status_Married marital-status_Single race_Amer-Indian race_Asian race_Black race_Hispanic race_White sex_Female sex_Male
0 39.0 13 40 0 1 0 0 1 0 0 0 0 1 0 1
1 50.0 13 13 0 0 1 1 0 0 0 0 0 1 0 1
2 38.0 9 40 1 0 0 0 1 0 0 0 0 1 0 1
3 53.0 7 40 1 0 0 1 0 0 0 1 0 0 0 1
4 28.0 13 40 1 0 0 1 0 0 0 1 0 0 1 0

Use scikit-learn to build classifiers uisng Naive Bayes (Gaussian), decision tree (using "entropy" as selection criteria), and linear discriminant analysis (LDA). For each of these perform 10-fold cross-validation (using cross-validation module in scikit-learn) and report the overall average accuracy.

In [263]:
from sklearn import tree, naive_bayes
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn import model_selection

Naive Bayes (Gaussian)

In [264]:
nbclf = naive_bayes.GaussianNB()
nbclf = nbclf.fit(df_new, df_target)

cv_scores = model_selection.cross_val_score(nbclf, df_new, df_target, cv=10)
cv_scores

print("Overall Accuracy on cross-validation is: %0.2f (+/- %0.2f)" % (cv_scores.mean(), cv_scores.std() * 2))
Overall Accuracy on cross-validation is: 0.72 (+/- 0.02)

Decision tree (using "entropy" as selection criteria)

In [265]:
treeclf = tree.DecisionTreeClassifier(criterion='entropy', min_samples_split=3)
treeclf = treeclf.fit(df_new, df_target)
cv_scores = model_selection.cross_val_score(treeclf, df_new, df_target, cv=10)
cv_scores

print("Overall Accuracy on cross-validation is: %0.2f (+/- %0.2f)" % (cv_scores.mean(), cv_scores.std() * 2))
Overall Accuracy on cross-validation is: 0.77 (+/- 0.03)

Linear Discriminant Analysis (LDA)

In [266]:
ldclf = LinearDiscriminantAnalysis()
ldclf = ldclf.fit(df_new, df_target)
cv_scores = model_selection.cross_val_score(ldclf, df_new, df_target, cv=10)
cv_scores

print("Overall Accuracy on cross-validation is: %0.2f (+/- %0.2f)" % (cv_scores.mean(), cv_scores.std() * 2))
C:\Users\rhame\Anaconda3\lib\site-packages\sklearn\discriminant_analysis.py:388: UserWarning: Variables are collinear.
  warnings.warn("Variables are collinear.")
C:\Users\rhame\Anaconda3\lib\site-packages\sklearn\discriminant_analysis.py:388: UserWarning: Variables are collinear.
  warnings.warn("Variables are collinear.")
C:\Users\rhame\Anaconda3\lib\site-packages\sklearn\discriminant_analysis.py:388: UserWarning: Variables are collinear.
  warnings.warn("Variables are collinear.")
C:\Users\rhame\Anaconda3\lib\site-packages\sklearn\discriminant_analysis.py:388: UserWarning: Variables are collinear.
  warnings.warn("Variables are collinear.")
C:\Users\rhame\Anaconda3\lib\site-packages\sklearn\discriminant_analysis.py:388: UserWarning: Variables are collinear.
  warnings.warn("Variables are collinear.")
C:\Users\rhame\Anaconda3\lib\site-packages\sklearn\discriminant_analysis.py:388: UserWarning: Variables are collinear.
  warnings.warn("Variables are collinear.")
C:\Users\rhame\Anaconda3\lib\site-packages\sklearn\discriminant_analysis.py:388: UserWarning: Variables are collinear.
  warnings.warn("Variables are collinear.")
C:\Users\rhame\Anaconda3\lib\site-packages\sklearn\discriminant_analysis.py:388: UserWarning: Variables are collinear.
  warnings.warn("Variables are collinear.")
C:\Users\rhame\Anaconda3\lib\site-packages\sklearn\discriminant_analysis.py:388: UserWarning: Variables are collinear.
  warnings.warn("Variables are collinear.")
Overall Accuracy on cross-validation is: 0.81 (+/- 0.02)
C:\Users\rhame\Anaconda3\lib\site-packages\sklearn\discriminant_analysis.py:388: UserWarning: Variables are collinear.
  warnings.warn("Variables are collinear.")
C:\Users\rhame\Anaconda3\lib\site-packages\sklearn\discriminant_analysis.py:388: UserWarning: Variables are collinear.
  warnings.warn("Variables are collinear.")

For the decision tree model (generated on the full training data), generate a visualization of tree and submit it as a separate file (png, jpg, or pdf) or embed it in the Jupyter Notebook.

In [276]:
import graphviz
from sklearn.tree import export_graphviz
from IPython.display import Image  
In [294]:
treeclf = tree.DecisionTreeClassifier(criterion='entropy', min_samples_split=3)
treeclf = treeclf.fit(df_new, df_target)
export_graphviz(treeclf,out_file='tree.dot', feature_names=df_new.columns )

with open("tree.dot") as f:
    dot_graph = f.read()
graphviz.Source(dot_graph)
Out[294]:
Tree 0 marital-status_Married <= 0.5 entropy = 0.806 samples = 9412 value = [7093, 2319] 1 education <= 12.5 entropy = 0.354 samples = 4675 value = [4363, 312] 0->1 True 1060 education <= 11.5 entropy = 0.983 samples = 4737 value = [2730, 2007] 0->1060 False 2 age <= 31.5 entropy = 0.189 samples = 3675 value = [3569, 106] 1->2 517 age <= 29.5 entropy = 0.734 samples = 1000 value = [794, 206] 1->517 3 hours-per-week <= 40.5 entropy = 0.067 samples = 1994 value = [1978, 16] 2->3 110 hours-per-week <= 42.5 entropy = 0.301 samples = 1681 value = [1591, 90] 2->110 4 age <= 21.5 entropy = 0.039 samples = 1675 value = [1668, 7] 3->4 49 hours-per-week <= 41.5 entropy = 0.185 samples = 319 value = [310, 9] 3->49 5 entropy = 0.0 samples = 692 value = [692, 0] 4->5 6 sex_Male <= 0.5 entropy = 0.061 samples = 983 value = [976, 7] 4->6 7 workclass_Self-emp <= 0.5 entropy = 0.023 samples = 456 value = [455, 1] 6->7 12 education <= 9.5 entropy = 0.09 samples = 527 value = [521, 6] 6->12 8 entropy = 0.0 samples = 450 value = [450, 0] 7->8 9 age <= 24.0 entropy = 0.65 samples = 6 value = [5, 1] 7->9 10 entropy = 0.0 samples = 1 value = [0, 1] 9->10 11 entropy = 0.0 samples = 5 value = [5, 0] 9->11 13 race_Asian <= 0.5 entropy = 0.031 samples = 316 value = [315, 1] 12->13 18 hours-per-week <= 27.5 entropy = 0.162 samples = 211 value = [206, 5] 12->18 14 entropy = 0.0 samples = 307 value = [307, 0] 13->14 15 education <= 8.0 entropy = 0.503 samples = 9 value = [8, 1] 13->15 16 entropy = 1.0 samples = 2 value = [1, 1] 15->16 17 entropy = 0.0 samples = 7 value = [7, 0] 15->17 19 entropy = 0.0 samples = 43 value = [43, 0] 18->19 20 race_White <= 0.5 entropy = 0.193 samples = 168 value = [163, 5] 18->20 21 entropy = 0.0 samples = 36 value = [36, 0] 20->21 22 age <= 22.5 entropy = 0.232 samples = 132 value = [127, 5] 20->22 23 entropy = 0.0 samples = 22 value = [22, 0] 22->23 24 workclass_Private <= 0.5 entropy = 0.267 samples = 110 value = [105, 5] 22->24 25 entropy = 0.0 samples = 16 value = [16, 0] 24->25 26 education <= 11.5 entropy = 0.3 samples = 94 value = [89, 5] 24->26 27 education <= 10.5 entropy = 0.331 samples = 82 value = [77, 5] 26->27 48 entropy = 0.0 samples = 12 value = [12, 0] 26->48 28 age <= 27.5 entropy = 0.258 samples = 69 value = [66, 3] 27->28 41 hours-per-week <= 32.5 entropy = 0.619 samples = 13 value = [11, 2] 27->41 29 age <= 23.5 entropy = 0.137 samples = 52 value = [51, 1] 28->29 34 age <= 28.5 entropy = 0.523 samples = 17 value = [15, 2] 28->34 30 hours-per-week <= 38.5 entropy = 0.297 samples = 19 value = [18, 1] 29->30 33 entropy = 0.0 samples = 33 value = [33, 0] 29->33 31 entropy = 0.0 samples = 6 value = [6, 0] 30->31 32 entropy = 0.391 samples = 13 value = [12, 1] 30->32 35 entropy = 0.811 samples = 4 value = [3, 1] 34->35 36 age <= 29.5 entropy = 0.391 samples = 13 value = [12, 1] 34->36 37 entropy = 0.0 samples = 3 value = [3, 0] 36->37 38 age <= 30.5 entropy = 0.469 samples = 10 value = [9, 1] 36->38 39 entropy = 0.544 samples = 8 value = [7, 1] 38->39 40 entropy = 0.0 samples = 2 value = [2, 0] 38->40 42 entropy = 0.0 samples = 1 value = [0, 1] 41->42 43 age <= 25.5 entropy = 0.414 samples = 12 value = [11, 1] 41->43 44 age <= 24.5 entropy = 0.722 samples = 5 value = [4, 1] 43->44 47 entropy = 0.0 samples = 7 value = [7, 0] 43->47 45 entropy = 0.0 samples = 3 value = [3, 0] 44->45 46 entropy = 1.0 samples = 2 value = [1, 1] 44->46 50 education <= 9.5 entropy = 0.722 samples = 5 value = [4, 1] 49->50 53 age <= 29.5 entropy = 0.171 samples = 314 value = [306, 8] 49->53 51 entropy = 0.0 samples = 3 value = [3, 0] 50->51 52 entropy = 1.0 samples = 2 value = [1, 1] 50->52 54 education <= 9.5 entropy = 0.136 samples = 263 value = [258, 5] 53->54 91 sex_Male <= 0.5 entropy = 0.323 samples = 51 value = [48, 3] 53->91 55 workclass_Private <= 0.5 entropy = 0.225 samples = 138 value = [133, 5] 54->55 90 entropy = 0.0 samples = 125 value = [125, 0] 54->90 56 age <= 25.0 entropy = 0.503 samples = 18 value = [16, 2] 55->56 65 education <= 4.5 entropy = 0.169 samples = 120 value = [117, 3] 55->65 57 race_Black <= 0.5 entropy = 0.764 samples = 9 value = [7, 2] 56->57 64 entropy = 0.0 samples = 9 value = [9, 0] 56->64 58 education <= 8.5 entropy = 0.544 samples = 8 value = [7, 1] 57->58 63 entropy = 0.0 samples = 1 value = [0, 1] 57->63 59 age <= 23.0 entropy = 0.918 samples = 3 value = [2, 1] 58->59 62 entropy = 0.0 samples = 5 value = [5, 0] 58->62 60 entropy = 0.0 samples = 1 value = [0, 1] 59->60 61 entropy = 0.0 samples = 2 value = [2, 0] 59->61 66 hours-per-week <= 55.0 entropy = 0.439 samples = 11 value = [10, 1] 65->66 71 age <= 23.5 entropy = 0.132 samples = 109 value = [107, 2] 65->71 67 entropy = 0.0 samples = 8 value = [8, 0] 66->67 68 age <= 19.5 entropy = 0.918 samples = 3 value = [2, 1] 66->68 69 entropy = 0.0 samples = 1 value = [0, 1] 68->69 70 entropy = 0.0 samples = 2 value = [2, 0] 68->70 72 entropy = 0.0 samples = 49 value = [49, 0] 71->72 73 age <= 24.5 entropy = 0.211 samples = 60 value = [58, 2] 71->73 74 hours-per-week <= 47.0 entropy = 0.544 samples = 8 value = [7, 1] 73->74 79 age <= 26.5 entropy = 0.137 samples = 52 value = [51, 1] 73->79 75 sex_Female <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 74->75 78 entropy = 0.0 samples = 5 value = [5, 0] 74->78 76 entropy = 0.0 samples = 2 value = [2, 0] 75->76 77 entropy = 0.0 samples = 1 value = [0, 1] 75->77 80 entropy = 0.0 samples = 24 value = [24, 0] 79->80 81 age <= 27.5 entropy = 0.222 samples = 28 value = [27, 1] 79->81 82 sex_Female <= 0.5 entropy = 0.414 samples = 12 value = [11, 1] 81->82 89 entropy = 0.0 samples = 16 value = [16, 0] 81->89 83 education <= 8.5 entropy = 0.65 samples = 6 value = [5, 1] 82->83 88 entropy = 0.0 samples = 6 value = [6, 0] 82->88 84 entropy = 0.0 samples = 3 value = [3, 0] 83->84 85 hours-per-week <= 49.0 entropy = 0.918 samples = 3 value = [2, 1] 83->85 86 entropy = 0.0 samples = 1 value = [1, 0] 85->86 87 entropy = 1.0 samples = 2 value = [1, 1] 85->87 92 entropy = 0.0 samples = 14 value = [14, 0] 91->92 93 workclass_Private <= 0.5 entropy = 0.406 samples = 37 value = [34, 3] 91->93 94 age <= 30.5 entropy = 0.918 samples = 3 value = [2, 1] 93->94 97 age <= 30.5 entropy = 0.323 samples = 34 value = [32, 2] 93->97 95 entropy = 0.0 samples = 2 value = [2, 0] 94->95 96 entropy = 0.0 samples = 1 value = [0, 1] 94->96 98 hours-per-week <= 51.0 entropy = 0.485 samples = 19 value = [17, 2] 97->98 109 entropy = 0.0 samples = 15 value = [15, 0] 97->109 99 hours-per-week <= 44.5 entropy = 0.619 samples = 13 value = [11, 2] 98->99 108 entropy = 0.0 samples = 6 value = [6, 0] 98->108 100 entropy = 0.0 samples = 3 value = [3, 0] 99->100 101 education <= 7.5 entropy = 0.722 samples = 10 value = [8, 2] 99->101 102 entropy = 0.0 samples = 2 value = [2, 0] 101->102 103 education <= 9.5 entropy = 0.811 samples = 8 value = [6, 2] 101->103 104 entropy = 1.0 samples = 2 value = [1, 1] 103->104 105 hours-per-week <= 49.0 entropy = 0.65 samples = 6 value = [5, 1] 103->105 106 entropy = 0.0 samples = 3 value = [3, 0] 105->106 107 entropy = 0.918 samples = 3 value = [2, 1] 105->107 111 hours-per-week <= 35.5 entropy = 0.227 samples = 1279 value = [1232, 47] 110->111 352 sex_Female <= 0.5 entropy = 0.491 samples = 402 value = [359, 43] 110->352 112 workclass_Self-emp <= 0.5 entropy = 0.062 samples = 273 value = [271, 2] 111->112 119 race_Black <= 0.5 entropy = 0.264 samples = 1006 value = [961, 45] 111->119 113 entropy = 0.0 samples = 242 value = [242, 0] 112->113 114 age <= 53.0 entropy = 0.345 samples = 31 value = [29, 2] 112->114 115 entropy = 0.0 samples = 21 value = [21, 0] 114->115 116 age <= 55.5 entropy = 0.722 samples = 10 value = [8, 2] 114->116 117 entropy = 0.0 samples = 2 value = [0, 2] 116->117 118 entropy = 0.0 samples = 8 value = [8, 0] 116->118 120 sex_Female <= 0.5 entropy = 0.297 samples = 817 value = [774, 43] 119->120 337 education <= 10.5 entropy = 0.085 samples = 189 value = [187, 2] 119->337 121 age <= 50.5 entropy = 0.382 samples = 349 value = [323, 26] 120->121 242 age <= 34.5 entropy = 0.225 samples = 468 value = [451, 17] 120->242 122 education <= 10.5 entropy = 0.328 samples = 283 value = [266, 17] 121->122 203 education <= 11.5 entropy = 0.575 samples = 66 value = [57, 9] 121->203 123 workclass_Public <= 0.5 entropy = 0.296 samples = 249 value = [236, 13] 122->123 188 workclass_Public <= 0.5 entropy = 0.523 samples = 34 value = [30, 4] 122->188 124 education <= 5.5 entropy = 0.323 samples = 204 value = [192, 12] 123->124 181 age <= 33.5 entropy = 0.154 samples = 45 value = [44, 1] 123->181 125 entropy = 0.0 samples = 8 value = [8, 0] 124->125 126 hours-per-week <= 39.0 entropy = 0.332 samples = 196 value = [184, 12] 124->126 127 entropy = 0.0 samples = 7 value = [7, 0] 126->127 128 race_Asian <= 0.5 entropy = 0.341 samples = 189 value = [177, 12] 126->128 129 age <= 49.5 entropy = 0.328 samples = 183 value = [172, 11] 128->129 178 age <= 43.0 entropy = 0.65 samples = 6 value = [5, 1] 128->178 130 age <= 44.5 entropy = 0.312 samples = 178 value = [168, 10] 129->130 175 education <= 9.5 entropy = 0.722 samples = 5 value = [4, 1] 129->175 131 age <= 41.5 entropy = 0.352 samples = 151 value = [141, 10] 130->131 174 entropy = 0.0 samples = 27 value = [27, 0] 130->174 132 education <= 8.5 entropy = 0.326 samples = 134 value = [126, 8] 131->132 167 education <= 7.5 entropy = 0.523 samples = 17 value = [15, 2] 131->167 133 entropy = 0.0 samples = 16 value = [16, 0] 132->133 134 education <= 9.5 entropy = 0.358 samples = 118 value = [110, 8] 132->134 135 age <= 33.5 entropy = 0.317 samples = 87 value = [82, 5] 134->135 156 age <= 32.5 entropy = 0.459 samples = 31 value = [28, 3] 134->156 136 entropy = 0.0 samples = 19 value = [19, 0] 135->136 137 age <= 39.5 entropy = 0.379 samples = 68 value = [63, 5] 135->137 138 workclass_Private <= 0.5 entropy = 0.475 samples = 49 value = [44, 5] 137->138 155 entropy = 0.0 samples = 19 value = [19, 0] 137->155 139 age <= 36.0 entropy = 0.811 samples = 4 value = [3, 1] 138->139 142 age <= 38.225 entropy = 0.433 samples = 45 value = [41, 4] 138->142 140 entropy = 0.0 samples = 1 value = [0, 1] 139->140 141 entropy = 0.0 samples = 3 value = [3, 0] 139->141 143 age <= 34.5 entropy = 0.337 samples = 32 value = [30, 2] 142->143 152 age <= 38.725 entropy = 0.619 samples = 13 value = [11, 2] 142->152 144 entropy = 0.65 samples = 6 value = [5, 1] 143->144 145 age <= 36.5 entropy = 0.235 samples = 26 value = [25, 1] 143->145 146 entropy = 0.0 samples = 10 value = [10, 0] 145->146 147 age <= 37.5 entropy = 0.337 samples = 16 value = [15, 1] 145->147 148 hours-per-week <= 41.0 entropy = 0.469 samples = 10 value = [9, 1] 147->148 151 entropy = 0.0 samples = 6 value = [6, 0] 147->151 149 entropy = 0.503 samples = 9 value = [8, 1] 148->149 150 entropy = 0.0 samples = 1 value = [1, 0] 148->150 153 entropy = 0.592 samples = 7 value = [6, 1] 152->153 154 entropy = 0.65 samples = 6 value = [5, 1] 152->154 157 entropy = 0.918 samples = 3 value = [2, 1] 156->157 158 age <= 36.5 entropy = 0.371 samples = 28 value = [26, 2] 156->158 159 entropy = 0.0 samples = 13 value = [13, 0] 158->159 160 age <= 37.5 entropy = 0.567 samples = 15 value = [13, 2] 158->160 161 entropy = 0.0 samples = 1 value = [0, 1] 160->161 162 age <= 40.5 entropy = 0.371 samples = 14 value = [13, 1] 160->162 163 entropy = 0.0 samples = 11 value = [11, 0] 162->163 164 workclass_Private <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 162->164 165 entropy = 0.0 samples = 2 value = [2, 0] 164->165 166 entropy = 0.0 samples = 1 value = [0, 1] 164->166 168 entropy = 0.0 samples = 1 value = [0, 1] 167->168 169 education <= 9.5 entropy = 0.337 samples = 16 value = [15, 1] 167->169 170 age <= 43.5 entropy = 0.592 samples = 7 value = [6, 1] 169->170 173 entropy = 0.0 samples = 9 value = [9, 0] 169->173 171 entropy = 0.0 samples = 4 value = [4, 0] 170->171 172 entropy = 0.918 samples = 3 value = [2, 1] 170->172 176 entropy = 1.0 samples = 2 value = [1, 1] 175->176 177 entropy = 0.0 samples = 3 value = [3, 0] 175->177 179 entropy = 0.0 samples = 4 value = [4, 0] 178->179 180 entropy = 1.0 samples = 2 value = [1, 1] 178->180 182 education <= 9.5 entropy = 0.592 samples = 7 value = [6, 1] 181->182 187 entropy = 0.0 samples = 38 value = [38, 0] 181->187 183 entropy = 0.0 samples = 4 value = [4, 0] 182->183 184 age <= 32.5 entropy = 0.918 samples = 3 value = [2, 1] 182->184 185 entropy = 0.0 samples = 2 value = [2, 0] 184->185 186 entropy = 0.0 samples = 1 value = [0, 1] 184->186 189 education <= 11.5 entropy = 0.235 samples = 26 value = [25, 1] 188->189 196 age <= 34.5 entropy = 0.954 samples = 8 value = [5, 3] 188->196 190 entropy = 0.0 samples = 15 value = [15, 0] 189->190 191 age <= 41.5 entropy = 0.439 samples = 11 value = [10, 1] 189->191 192 age <= 39.5 entropy = 0.722 samples = 5 value = [4, 1] 191->192 195 entropy = 0.0 samples = 6 value = [6, 0] 191->195 193 entropy = 0.0 samples = 4 value = [4, 0] 192->193 194 entropy = 0.0 samples = 1 value = [0, 1] 192->194 197 entropy = 0.0 samples = 3 value = [3, 0] 196->197 198 education <= 11.5 entropy = 0.971 samples = 5 value = [2, 3] 196->198 199 entropy = 0.0 samples = 2 value = [0, 2] 198->199 200 hours-per-week <= 39.0 entropy = 0.918 samples = 3 value = [2, 1] 198->200 201 entropy = 0.0 samples = 1 value = [0, 1] 200->201 202 entropy = 0.0 samples = 2 value = [2, 0] 200->202 204 race_Hispanic <= 0.5 entropy = 0.538 samples = 65 value = [57, 8] 203->204 241 entropy = 0.0 samples = 1 value = [0, 1] 203->241 205 education <= 9.5 entropy = 0.498 samples = 64 value = [57, 7] 204->205 240 entropy = 0.0 samples = 1 value = [0, 1] 204->240 206 education <= 4.5 entropy = 0.348 samples = 46 value = [43, 3] 205->206 223 workclass_Private <= 0.5 entropy = 0.764 samples = 18 value = [14, 4] 205->223 207 entropy = 0.0 samples = 9 value = [9, 0] 206->207 208 education <= 5.5 entropy = 0.406 samples = 37 value = [34, 3] 206->208 209 workclass_Private <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 208->209 212 age <= 53.5 entropy = 0.323 samples = 34 value = [32, 2] 208->212 210 entropy = 0.0 samples = 1 value = [1, 0] 209->210 211 entropy = 1.0 samples = 2 value = [1, 1] 209->211 213 education <= 8.5 entropy = 0.567 samples = 15 value = [13, 2] 212->213 222 entropy = 0.0 samples = 19 value = [19, 0] 212->222 214 entropy = 0.0 samples = 3 value = [3, 0] 213->214 215 hours-per-week <= 39.0 entropy = 0.65 samples = 12 value = [10, 2] 213->215 216 entropy = 0.0 samples = 3 value = [3, 0] 215->216 217 workclass_Self-emp <= 0.5 entropy = 0.764 samples = 9 value = [7, 2] 215->217 218 age <= 52.5 entropy = 0.592 samples = 7 value = [6, 1] 217->218 221 entropy = 1.0 samples = 2 value = [1, 1] 217->221 219 entropy = 0.0 samples = 4 value = [4, 0] 218->219 220 entropy = 0.918 samples = 3 value = [2, 1] 218->220 224 entropy = 0.0 samples = 5 value = [5, 0] 223->224 225 age <= 57.0 entropy = 0.89 samples = 13 value = [9, 4] 223->225 226 age <= 55.0 entropy = 0.985 samples = 7 value = [4, 3] 225->226 235 age <= 59.5 entropy = 0.65 samples = 6 value = [5, 1] 225->235 227 age <= 51.5 entropy = 0.918 samples = 6 value = [4, 2] 226->227 234 entropy = 0.0 samples = 1 value = [0, 1] 226->234 228 entropy = 0.0 samples = 1 value = [1, 0] 227->228 229 age <= 52.5 entropy = 0.971 samples = 5 value = [3, 2] 227->229 230 entropy = 1.0 samples = 2 value = [1, 1] 229->230 231 age <= 53.5 entropy = 0.918 samples = 3 value = [2, 1] 229->231 232 entropy = 0.0 samples = 1 value = [1, 0] 231->232 233 entropy = 1.0 samples = 2 value = [1, 1] 231->233 236 entropy = 0.0 samples = 3 value = [3, 0] 235->236 237 hours-per-week <= 41.0 entropy = 0.918 samples = 3 value = [2, 1] 235->237 238 entropy = 1.0 samples = 2 value = [1, 1] 237->238 239 entropy = 0.0 samples = 1 value = [1, 0] 237->239 243 entropy = 0.0 samples = 60 value = [60, 0] 242->243 244 age <= 61.5 entropy = 0.25 samples = 408 value = [391, 17] 242->244 245 race_White <= 0.5 entropy = 0.224 samples = 387 value = [373, 14] 244->245 326 education <= 9.5 entropy = 0.592 samples = 21 value = [18, 3] 244->326 246 education <= 9.5 entropy = 0.516 samples = 26 value = [23, 3] 245->246 261 hours-per-week <= 37.5 entropy = 0.197 samples = 361 value = [350, 11] 245->261 247 entropy = 0.0 samples = 10 value = [10, 0] 246->247 248 age <= 45.5 entropy = 0.696 samples = 16 value = [13, 3] 246->248 249 race_Asian <= 0.5 entropy = 0.779 samples = 13 value = [10, 3] 248->249 260 entropy = 0.0 samples = 3 value = [3, 0] 248->260 250 workclass_Public <= 0.5 entropy = 0.544 samples = 8 value = [7, 1] 249->250 255 education <= 11.0 entropy = 0.971 samples = 5 value = [3, 2] 249->255 251 entropy = 0.0 samples = 5 value = [5, 0] 250->251 252 age <= 40.0 entropy = 0.918 samples = 3 value = [2, 1] 250->252 253 entropy = 1.0 samples = 2 value = [1, 1] 252->253 254 entropy = 0.0 samples = 1 value = [1, 0] 252->254 256 age <= 38.0 entropy = 0.811 samples = 4 value = [3, 1] 255->256 259 entropy = 0.0 samples = 1 value = [0, 1] 255->259 257 entropy = 1.0 samples = 2 value = [1, 1] 256->257 258 entropy = 0.0 samples = 2 value = [2, 0] 256->258 262 age <= 40.5 entropy = 0.523 samples = 17 value = [15, 2] 261->262 273 education <= 10.5 entropy = 0.175 samples = 344 value = [335, 9] 261->273 263 entropy = 0.0 samples = 6 value = [6, 0] 262->263 264 age <= 50.0 entropy = 0.684 samples = 11 value = [9, 2] 262->264 265 education <= 10.5 entropy = 0.863 samples = 7 value = [5, 2] 264->265 272 entropy = 0.0 samples = 4 value = [4, 0] 264->272 266 education <= 9.5 entropy = 0.918 samples = 6 value = [4, 2] 265->266 271 entropy = 0.0 samples = 1 value = [1, 0] 265->271 267 age <= 47.5 entropy = 0.811 samples = 4 value = [3, 1] 266->267 270 entropy = 1.0 samples = 2 value = [1, 1] 266->270 268 entropy = 0.0 samples = 2 value = [2, 0] 267->268 269 entropy = 1.0 samples = 2 value = [1, 1] 267->269 274 age <= 54.5 entropy = 0.141 samples = 300 value = [294, 6] 273->274 311 age <= 35.5 entropy = 0.359 samples = 44 value = [41, 3] 273->311 275 age <= 44.5 entropy = 0.094 samples = 250 value = [247, 3] 274->275 294 age <= 58.5 entropy = 0.327 samples = 50 value = [47, 3] 274->294 276 age <= 38.225 entropy = 0.143 samples = 148 value = [145, 3] 275->276 293 entropy = 0.0 samples = 102 value = [102, 0] 275->293 277 entropy = 0.0 samples = 57 value = [57, 0] 276->277 278 age <= 39.5 entropy = 0.209 samples = 91 value = [88, 3] 276->278 279 education <= 7.5 entropy = 0.454 samples = 21 value = [19, 2] 278->279 288 age <= 43.5 entropy = 0.108 samples = 70 value = [69, 1] 278->288 280 education <= 4.5 entropy = 0.918 samples = 3 value = [2, 1] 279->280 283 education <= 9.5 entropy = 0.31 samples = 18 value = [17, 1] 279->283 281 entropy = 0.0 samples = 1 value = [1, 0] 280->281 282 entropy = 1.0 samples = 2 value = [1, 1] 280->282 284 entropy = 0.0 samples = 12 value = [12, 0] 283->284 285 age <= 38.725 entropy = 0.65 samples = 6 value = [5, 1] 283->285 286 entropy = 0.0 samples = 1 value = [1, 0] 285->286 287 entropy = 0.722 samples = 5 value = [4, 1] 285->287 289 entropy = 0.0 samples = 63 value = [63, 0] 288->289 290 education <= 9.5 entropy = 0.592 samples = 7 value = [6, 1] 288->290 291 entropy = 0.722 samples = 5 value = [4, 1] 290->291 292 entropy = 0.0 samples = 2 value = [2, 0] 290->292 295 education <= 8.0 entropy = 0.439 samples = 33 value = [30, 3] 294->295 310 entropy = 0.0 samples = 17 value = [17, 0] 294->310 296 entropy = 0.0 samples = 6 value = [6, 0] 295->296 297 education <= 9.5 entropy = 0.503 samples = 27 value = [24, 3] 295->297 298 age <= 56.5 entropy = 0.575 samples = 22 value = [19, 3] 297->298 309 entropy = 0.0 samples = 5 value = [5, 0] 297->309 299 workclass_Private <= 0.5 entropy = 0.391 samples = 13 value = [12, 1] 298->299 304 workclass_Private <= 0.5 entropy = 0.764 samples = 9 value = [7, 2] 298->304 300 age <= 55.5 entropy = 0.918 samples = 3 value = [2, 1] 299->300 303 entropy = 0.0 samples = 10 value = [10, 0] 299->303 301 entropy = 1.0 samples = 2 value = [1, 1] 300->301 302 entropy = 0.0 samples = 1 value = [1, 0] 300->302 305 entropy = 0.0 samples = 2 value = [2, 0] 304->305 306 age <= 57.5 entropy = 0.863 samples = 7 value = [5, 2] 304->306 307 entropy = 0.811 samples = 4 value = [3, 1] 306->307 308 entropy = 0.918 samples = 3 value = [2, 1] 306->308 312 entropy = 1.0 samples = 2 value = [1, 1] 311->312 313 age <= 41.5 entropy = 0.276 samples = 42 value = [40, 2] 311->313 314 entropy = 0.0 samples = 19 value = [19, 0] 313->314 315 age <= 45.5 entropy = 0.426 samples = 23 value = [21, 2] 313->315 316 workclass_Public <= 0.5 entropy = 0.863 samples = 7 value = [5, 2] 315->316 325 entropy = 0.0 samples = 16 value = [16, 0] 315->325 317 hours-per-week <= 39.0 entropy = 0.918 samples = 6 value = [4, 2] 316->317 324 entropy = 0.0 samples = 1 value = [1, 0] 316->324 318 entropy = 0.0 samples = 1 value = [1, 0] 317->318 319 age <= 44.0 entropy = 0.971 samples = 5 value = [3, 2] 317->319 320 education <= 11.5 entropy = 0.811 samples = 4 value = [3, 1] 319->320 323 entropy = 0.0 samples = 1 value = [0, 1] 319->323 321 entropy = 0.0 samples = 2 value = [2, 0] 320->321 322 entropy = 1.0 samples = 2 value = [1, 1] 320->322 327 entropy = 0.0 samples = 10 value = [10, 0] 326->327 328 age <= 66.5 entropy = 0.845 samples = 11 value = [8, 3] 326->328 329 hours-per-week <= 39.5 entropy = 0.985 samples = 7 value = [4, 3] 328->329 336 entropy = 0.0 samples = 4 value = [4, 0] 328->336 330 entropy = 0.0 samples = 1 value = [0, 1] 329->330 331 age <= 65.5 entropy = 0.918 samples = 6 value = [4, 2] 329->331 332 education <= 11.0 entropy = 0.722 samples = 5 value = [4, 1] 331->332 335 entropy = 0.0 samples = 1 value = [0, 1] 331->335 333 entropy = 0.0 samples = 4 value = [4, 0] 332->333 334 entropy = 0.0 samples = 1 value = [0, 1] 332->334 338 age <= 46.5 entropy = 0.052 samples = 172 value = [171, 1] 337->338 347 age <= 33.5 entropy = 0.323 samples = 17 value = [16, 1] 337->347 339 entropy = 0.0 samples = 115 value = [115, 0] 338->339 340 age <= 47.5 entropy = 0.127 samples = 57 value = [56, 1] 338->340 341 workclass_Private <= 0.5 entropy = 0.439 samples = 11 value = [10, 1] 340->341 346 entropy = 0.0 samples = 46 value = [46, 0] 340->346 342 entropy = 0.0 samples = 6 value = [6, 0] 341->342 343 sex_Female <= 0.5 entropy = 0.722 samples = 5 value = [4, 1] 341->343 344 entropy = 0.0 samples = 2 value = [2, 0] 343->344 345 entropy = 0.918 samples = 3 value = [2, 1] 343->345 348 workclass_Public <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 347->348 351 entropy = 0.0 samples = 14 value = [14, 0] 347->351 349 entropy = 0.0 samples = 1 value = [1, 0] 348->349 350 entropy = 1.0 samples = 2 value = [1, 1] 348->350 353 race_White <= 0.5 entropy = 0.559 samples = 253 value = [220, 33] 352->353 476 education <= 10.5 entropy = 0.355 samples = 149 value = [139, 10] 352->476 354 entropy = 0.0 samples = 16 value = [16, 0] 353->354 355 education <= 11.5 entropy = 0.582 samples = 237 value = [204, 33] 353->355 356 age <= 39.5 entropy = 0.597 samples = 228 value = [195, 33] 355->356 475 entropy = 0.0 samples = 9 value = [9, 0] 355->475 357 education <= 8.5 entropy = 0.509 samples = 124 value = [110, 14] 356->357 414 workclass_Public <= 0.5 entropy = 0.686 samples = 104 value = [85, 19] 356->414 358 age <= 36.5 entropy = 0.9 samples = 19 value = [13, 6] 357->358 371 age <= 38.225 entropy = 0.389 samples = 105 value = [97, 8] 357->371 359 hours-per-week <= 43.5 entropy = 0.65 samples = 12 value = [10, 2] 358->359 364 hours-per-week <= 46.5 entropy = 0.985 samples = 7 value = [3, 4] 358->364 360 entropy = 0.0 samples = 1 value = [0, 1] 359->360 361 education <= 7.5 entropy = 0.439 samples = 11 value = [10, 1] 359->361 362 entropy = 0.0 samples = 9 value = [9, 0] 361->362 363 entropy = 1.0 samples = 2 value = [1, 1] 361->363 365 entropy = 0.0 samples = 1 value = [1, 0] 364->365 366 hours-per-week <= 62.5 entropy = 0.918 samples = 6 value = [2, 4] 364->366 367 entropy = 0.0 samples = 3 value = [0, 3] 366->367 368 hours-per-week <= 77.5 entropy = 0.918 samples = 3 value = [2, 1] 366->368 369 entropy = 0.0 samples = 2 value = [2, 0] 368->369 370 entropy = 0.0 samples = 1 value = [0, 1] 368->370 372 hours-per-week <= 61.0 entropy = 0.436 samples = 89 value = [81, 8] 371->372 413 entropy = 0.0 samples = 16 value = [16, 0] 371->413 373 age <= 34.5 entropy = 0.481 samples = 77 value = [69, 8] 372->373 412 entropy = 0.0 samples = 12 value = [12, 0] 372->412 374 age <= 33.5 entropy = 0.592 samples = 35 value = [30, 5] 373->374 395 workclass_Private <= 0.5 entropy = 0.371 samples = 42 value = [39, 3] 373->395 375 hours-per-week <= 48.0 entropy = 0.426 samples = 23 value = [21, 2] 374->375 384 workclass_Public <= 0.5 entropy = 0.811 samples = 12 value = [9, 3] 374->384 376 entropy = 0.0 samples = 6 value = [6, 0] 375->376 377 education <= 10.5 entropy = 0.523 samples = 17 value = [15, 2] 375->377 378 hours-per-week <= 57.5 entropy = 0.353 samples = 15 value = [14, 1] 377->378 383 entropy = 1.0 samples = 2 value = [1, 1] 377->383 379 entropy = 0.0 samples = 11 value = [11, 0] 378->379 380 age <= 32.5 entropy = 0.811 samples = 4 value = [3, 1] 378->380 381 entropy = 0.0 samples = 2 value = [2, 0] 380->381 382 entropy = 1.0 samples = 2 value = [1, 1] 380->382 385 hours-per-week <= 44.5 entropy = 0.684 samples = 11 value = [9, 2] 384->385 394 entropy = 0.0 samples = 1 value = [0, 1] 384->394 386 entropy = 0.0 samples = 2 value = [2, 0] 385->386 387 hours-per-week <= 47.5 entropy = 0.764 samples = 9 value = [7, 2] 385->387 388 entropy = 1.0 samples = 2 value = [1, 1] 387->388 389 education <= 9.5 entropy = 0.592 samples = 7 value = [6, 1] 387->389 390 entropy = 0.0 samples = 3 value = [3, 0] 389->390 391 education <= 10.5 entropy = 0.811 samples = 4 value = [3, 1] 389->391 392 entropy = 0.918 samples = 3 value = [2, 1] 391->392 393 entropy = 0.0 samples = 1 value = [1, 0] 391->393 396 entropy = 0.0 samples = 11 value = [11, 0] 395->396 397 age <= 35.5 entropy = 0.459 samples = 31 value = [28, 3] 395->397 398 entropy = 0.0 samples = 9 value = [9, 0] 397->398 399 hours-per-week <= 53.5 entropy = 0.575 samples = 22 value = [19, 3] 397->399 400 hours-per-week <= 46.5 entropy = 0.371 samples = 14 value = [13, 1] 399->400 405 hours-per-week <= 56.5 entropy = 0.811 samples = 8 value = [6, 2] 399->405 401 education <= 9.5 entropy = 0.65 samples = 6 value = [5, 1] 400->401 404 entropy = 0.0 samples = 8 value = [8, 0] 400->404 402 entropy = 0.918 samples = 3 value = [2, 1] 401->402 403 entropy = 0.0 samples = 3 value = [3, 0] 401->403 406 entropy = 1.0 samples = 2 value = [1, 1] 405->406 407 age <= 36.5 entropy = 0.65 samples = 6 value = [5, 1] 405->407 408 education <= 9.5 entropy = 0.918 samples = 3 value = [2, 1] 407->408 411 entropy = 0.0 samples = 3 value = [3, 0] 407->411 409 entropy = 1.0 samples = 2 value = [1, 1] 408->409 410 entropy = 0.0 samples = 1 value = [1, 0] 408->410 415 hours-per-week <= 49.0 entropy = 0.654 samples = 101 value = [84, 17] 414->415 472 education <= 6.5 entropy = 0.918 samples = 3 value = [1, 2] 414->472 416 hours-per-week <= 45.5 entropy = 0.811 samples = 36 value = [27, 9] 415->416 443 hours-per-week <= 54.5 entropy = 0.538 samples = 65 value = [57, 8] 415->443 417 age <= 60.5 entropy = 0.619 samples = 26 value = [22, 4] 416->417 432 education <= 9.5 entropy = 1.0 samples = 10 value = [5, 5] 416->432 418 hours-per-week <= 44.5 entropy = 0.529 samples = 25 value = [22, 3] 417->418 431 entropy = 0.0 samples = 1 value = [0, 1] 417->431 419 entropy = 1.0 samples = 2 value = [1, 1] 418->419 420 education <= 9.5 entropy = 0.426 samples = 23 value = [21, 2] 418->420 421 entropy = 0.0 samples = 10 value = [10, 0] 420->421 422 age <= 43.5 entropy = 0.619 samples = 13 value = [11, 2] 420->422 423 entropy = 0.0 samples = 5 value = [5, 0] 422->423 424 age <= 44.5 entropy = 0.811 samples = 8 value = [6, 2] 422->424 425 entropy = 0.0 samples = 1 value = [0, 1] 424->425 426 age <= 49.5 entropy = 0.592 samples = 7 value = [6, 1] 424->426 427 age <= 47.5 entropy = 0.811 samples = 4 value = [3, 1] 426->427 430 entropy = 0.0 samples = 3 value = [3, 0] 426->430 428 entropy = 0.0 samples = 2 value = [2, 0] 427->428 429 entropy = 1.0 samples = 2 value = [1, 1] 427->429 433 education <= 7.5 entropy = 0.722 samples = 5 value = [4, 1] 432->433 438 hours-per-week <= 47.5 entropy = 0.722 samples = 5 value = [1, 4] 432->438 434 entropy = 0.0 samples = 2 value = [2, 0] 433->434 435 hours-per-week <= 47.5 entropy = 0.918 samples = 3 value = [2, 1] 433->435 436 entropy = 0.0 samples = 2 value = [2, 0] 435->436 437 entropy = 0.0 samples = 1 value = [0, 1] 435->437 439 entropy = 0.0 samples = 2 value = [0, 2] 438->439 440 age <= 48.0 entropy = 0.918 samples = 3 value = [1, 2] 438->440 441 entropy = 1.0 samples = 2 value = [1, 1] 440->441 442 entropy = 0.0 samples = 1 value = [0, 1] 440->442 444 entropy = 0.0 samples = 28 value = [28, 0] 443->444 445 age <= 58.0 entropy = 0.753 samples = 37 value = [29, 8] 443->445 446 hours-per-week <= 72.5 entropy = 0.837 samples = 30 value = [22, 8] 445->446 471 entropy = 0.0 samples = 7 value = [7, 0] 445->471 447 age <= 56.5 entropy = 0.877 samples = 27 value = [19, 8] 446->447 470 entropy = 0.0 samples = 3 value = [3, 0] 446->470 448 education <= 8.0 entropy = 0.84 samples = 26 value = [19, 7] 447->448 469 entropy = 0.0 samples = 1 value = [0, 1] 447->469 449 entropy = 0.0 samples = 3 value = [3, 0] 448->449 450 age <= 54.0 entropy = 0.887 samples = 23 value = [16, 7] 448->450 451 hours-per-week <= 65.0 entropy = 0.845 samples = 22 value = [16, 6] 450->451 468 entropy = 0.0 samples = 1 value = [0, 1] 450->468 452 workclass_Self-emp <= 0.5 entropy = 0.764 samples = 18 value = [14, 4] 451->452 465 education <= 9.5 entropy = 1.0 samples = 4 value = [2, 2] 451->465 453 hours-per-week <= 56.5 entropy = 0.918 samples = 9 value = [6, 3] 452->453 460 age <= 47.5 entropy = 0.503 samples = 9 value = [8, 1] 452->460 454 age <= 49.5 entropy = 0.65 samples = 6 value = [5, 1] 453->454 457 education <= 9.5 entropy = 0.918 samples = 3 value = [1, 2] 453->457 455 entropy = 0.0 samples = 4 value = [4, 0] 454->455 456 entropy = 1.0 samples = 2 value = [1, 1] 454->456 458 entropy = 0.0 samples = 1 value = [1, 0] 457->458 459 entropy = 0.0 samples = 2 value = [0, 2] 457->459 461 entropy = 0.0 samples = 6 value = [6, 0] 460->461 462 education <= 9.5 entropy = 0.918 samples = 3 value = [2, 1] 460->462 463 entropy = 0.0 samples = 1 value = [0, 1] 462->463 464 entropy = 0.0 samples = 2 value = [2, 0] 462->464 466 entropy = 0.0 samples = 2 value = [2, 0] 465->466 467 entropy = 0.0 samples = 2 value = [0, 2] 465->467 473 entropy = 0.0 samples = 1 value = [1, 0] 472->473 474 entropy = 0.0 samples = 2 value = [0, 2] 472->474 477 hours-per-week <= 51.0 entropy = 0.244 samples = 124 value = [119, 5] 476->477 504 workclass_Public <= 0.5 entropy = 0.722 samples = 25 value = [20, 5] 476->504 478 hours-per-week <= 49.5 entropy = 0.337 samples = 80 value = [75, 5] 477->478 503 entropy = 0.0 samples = 44 value = [44, 0] 477->503 479 hours-per-week <= 44.5 entropy = 0.144 samples = 49 value = [48, 1] 478->479 486 age <= 44.5 entropy = 0.555 samples = 31 value = [27, 4] 478->486 480 age <= 40.5 entropy = 0.414 samples = 12 value = [11, 1] 479->480 485 entropy = 0.0 samples = 37 value = [37, 0] 479->485 481 age <= 39.0 entropy = 0.918 samples = 3 value = [2, 1] 480->481 484 entropy = 0.0 samples = 9 value = [9, 0] 480->484 482 entropy = 0.0 samples = 1 value = [1, 0] 481->482 483 entropy = 1.0 samples = 2 value = [1, 1] 481->483 487 age <= 32.5 entropy = 0.297 samples = 19 value = [18, 1] 486->487 492 age <= 45.5 entropy = 0.811 samples = 12 value = [9, 3] 486->492 488 education <= 9.5 entropy = 0.918 samples = 3 value = [2, 1] 487->488 491 entropy = 0.0 samples = 16 value = [16, 0] 487->491 489 entropy = 0.0 samples = 1 value = [1, 0] 488->489 490 entropy = 1.0 samples = 2 value = [1, 1] 488->490 493 entropy = 0.0 samples = 1 value = [0, 1] 492->493 494 age <= 50.5 entropy = 0.684 samples = 11 value = [9, 2] 492->494 495 entropy = 0.0 samples = 3 value = [3, 0] 494->495 496 age <= 55.5 entropy = 0.811 samples = 8 value = [6, 2] 494->496 497 age <= 53.5 entropy = 0.971 samples = 5 value = [3, 2] 496->497 502 entropy = 0.0 samples = 3 value = [3, 0] 496->502 498 workclass_Private <= 0.5 entropy = 0.811 samples = 4 value = [3, 1] 497->498 501 entropy = 0.0 samples = 1 value = [0, 1] 497->501 499 entropy = 0.0 samples = 2 value = [2, 0] 498->499 500 entropy = 1.0 samples = 2 value = [1, 1] 498->500 505 age <= 38.225 entropy = 0.592 samples = 21 value = [18, 3] 504->505 514 hours-per-week <= 53.0 entropy = 1.0 samples = 4 value = [2, 2] 504->514 506 age <= 37.5 entropy = 0.881 samples = 10 value = [7, 3] 505->506 513 entropy = 0.0 samples = 11 value = [11, 0] 505->513 507 race_Asian <= 0.5 entropy = 0.764 samples = 9 value = [7, 2] 506->507 512 entropy = 0.0 samples = 1 value = [0, 1] 506->512 508 age <= 36.5 entropy = 0.544 samples = 8 value = [7, 1] 507->508 511 entropy = 0.0 samples = 1 value = [0, 1] 507->511 509 entropy = 0.0 samples = 6 value = [6, 0] 508->509 510 entropy = 1.0 samples = 2 value = [1, 1] 508->510 515 entropy = 0.0 samples = 2 value = [2, 0] 514->515 516 entropy = 0.0 samples = 2 value = [0, 2] 514->516 518 hours-per-week <= 39.5 entropy = 0.228 samples = 352 value = [339, 13] 517->518 593 education <= 14.5 entropy = 0.879 samples = 648 value = [455, 193] 517->593 519 entropy = 0.0 samples = 87 value = [87, 0] 518->519 520 workclass_Public <= 0.5 entropy = 0.282 samples = 265 value = [252, 13] 518->520 521 education <= 13.5 entropy = 0.323 samples = 221 value = [208, 13] 520->521 592 entropy = 0.0 samples = 44 value = [44, 0] 520->592 522 age <= 22.5 entropy = 0.268 samples = 197 value = [188, 9] 521->522 571 education <= 15.5 entropy = 0.65 samples = 24 value = [20, 4] 521->571 523 hours-per-week <= 52.5 entropy = 0.619 samples = 13 value = [11, 2] 522->523 532 sex_Male <= 0.5 entropy = 0.233 samples = 184 value = [177, 7] 522->532 524 sex_Female <= 0.5 entropy = 0.469 samples = 10 value = [9, 1] 523->524 529 hours-per-week <= 57.5 entropy = 0.918 samples = 3 value = [2, 1] 523->529 525 entropy = 0.0 samples = 5 value = [5, 0] 524->525 526 hours-per-week <= 45.0 entropy = 0.722 samples = 5 value = [4, 1] 524->526 527 entropy = 0.811 samples = 4 value = [3, 1] 526->527 528 entropy = 0.0 samples = 1 value = [1, 0] 526->528 530 entropy = 1.0 samples = 2 value = [1, 1] 529->530 531 entropy = 0.0 samples = 1 value = [1, 0] 529->531 533 age <= 25.5 entropy = 0.097 samples = 80 value = [79, 1] 532->533 542 hours-per-week <= 49.0 entropy = 0.318 samples = 104 value = [98, 6] 532->542 534 age <= 24.5 entropy = 0.176 samples = 38 value = [37, 1] 533->534 541 entropy = 0.0 samples = 42 value = [42, 0] 533->541 535 entropy = 0.0 samples = 26 value = [26, 0] 534->535 536 race_Asian <= 0.5 entropy = 0.414 samples = 12 value = [11, 1] 534->536 537 hours-per-week <= 45.0 entropy = 0.469 samples = 10 value = [9, 1] 536->537 540 entropy = 0.0 samples = 2 value = [2, 0] 536->540 538 entropy = 0.544 samples = 8 value = [7, 1] 537->538 539 entropy = 0.0 samples = 2 value = [2, 0] 537->539 543 race_Asian <= 0.5 entropy = 0.238 samples = 77 value = [74, 3] 542->543 558 age <= 24.5 entropy = 0.503 samples = 27 value = [24, 3] 542->558 544 workclass_Self-emp <= 0.5 entropy = 0.181 samples = 73 value = [71, 2] 543->544 555 age <= 26.5 entropy = 0.811 samples = 4 value = [3, 1] 543->555 545 age <= 27.5 entropy = 0.112 samples = 67 value = [66, 1] 544->545 552 age <= 23.5 entropy = 0.65 samples = 6 value = [5, 1] 544->552 546 entropy = 0.0 samples = 49 value = [49, 0] 545->546 547 age <= 28.5 entropy = 0.31 samples = 18 value = [17, 1] 545->547 548 hours-per-week <= 41.5 entropy = 0.503 samples = 9 value = [8, 1] 547->548 551 entropy = 0.0 samples = 9 value = [9, 0] 547->551 549 entropy = 0.722 samples = 5 value = [4, 1] 548->549 550 entropy = 0.0 samples = 4 value = [4, 0] 548->550 553 entropy = 0.0 samples = 1 value = [0, 1] 552->553 554 entropy = 0.0 samples = 5 value = [5, 0] 552->554 556 entropy = 0.0 samples = 3 value = [3, 0] 555->556 557 entropy = 0.0 samples = 1 value = [0, 1] 555->557 559 entropy = 0.0 samples = 11 value = [11, 0] 558->559 560 hours-per-week <= 52.5 entropy = 0.696 samples = 16 value = [13, 3] 558->560 561 age <= 26.5 entropy = 0.881 samples = 10 value = [7, 3] 560->561 570 entropy = 0.0 samples = 6 value = [6, 0] 560->570 562 age <= 25.5 entropy = 0.918 samples = 3 value = [1, 2] 561->562 565 age <= 28.5 entropy = 0.592 samples = 7 value = [6, 1] 561->565 563 entropy = 1.0 samples = 2 value = [1, 1] 562->563 564 entropy = 0.0 samples = 1 value = [0, 1] 562->564 566 entropy = 0.0 samples = 4 value = [4, 0] 565->566 567 workclass_Self-emp <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 565->567 568 entropy = 1.0 samples = 2 value = [1, 1] 567->568 569 entropy = 0.0 samples = 1 value = [1, 0] 567->569 572 age <= 25.5 entropy = 0.742 samples = 19 value = [15, 4] 571->572 591 entropy = 0.0 samples = 5 value = [5, 0] 571->591 573 entropy = 0.0 samples = 4 value = [4, 0] 572->573 574 education <= 14.5 entropy = 0.837 samples = 15 value = [11, 4] 572->574 575 hours-per-week <= 45.0 entropy = 0.722 samples = 10 value = [8, 2] 574->575 586 age <= 27.5 entropy = 0.971 samples = 5 value = [3, 2] 574->586 576 workclass_Self-emp <= 0.5 entropy = 0.863 samples = 7 value = [5, 2] 575->576 585 entropy = 0.0 samples = 3 value = [3, 0] 575->585 577 race_White <= 0.5 entropy = 0.918 samples = 6 value = [4, 2] 576->577 584 entropy = 0.0 samples = 1 value = [1, 0] 576->584 578 entropy = 0.0 samples = 1 value = [1, 0] 577->578 579 age <= 26.5 entropy = 0.971 samples = 5 value = [3, 2] 577->579 580 entropy = 1.0 samples = 2 value = [1, 1] 579->580 581 age <= 27.5 entropy = 0.918 samples = 3 value = [2, 1] 579->581 582 entropy = 0.0 samples = 1 value = [1, 0] 581->582 583 entropy = 1.0 samples = 2 value = [1, 1] 581->583 587 entropy = 0.0 samples = 1 value = [0, 1] 586->587 588 age <= 28.5 entropy = 0.811 samples = 4 value = [3, 1] 586->588 589 entropy = 0.0 samples = 3 value = [3, 0] 588->589 590 entropy = 0.0 samples = 1 value = [0, 1] 588->590 594 hours-per-week <= 42.5 entropy = 0.821 samples = 582 value = [433, 149] 593->594 1013 hours-per-week <= 75.0 entropy = 0.918 samples = 66 value = [22, 44] 593->1013 595 age <= 44.5 entropy = 0.666 samples = 351 value = [290, 61] 594->595 806 age <= 49.5 entropy = 0.959 samples = 231 value = [143, 88] 594->806 596 hours-per-week <= 10.0 entropy = 0.557 samples = 239 value = [208, 31] 595->596 719 hours-per-week <= 39.0 entropy = 0.838 samples = 112 value = [82, 30] 595->719 597 entropy = 0.0 samples = 1 value = [0, 1] 596->597 598 hours-per-week <= 31.0 entropy = 0.547 samples = 238 value = [208, 30] 596->598 599 entropy = 0.0 samples = 21 value = [21, 0] 598->599 600 age <= 37.5 entropy = 0.58 samples = 217 value = [187, 30] 598->600 601 age <= 33.5 entropy = 0.653 samples = 113 value = [94, 19] 600->601 670 age <= 38.725 entropy = 0.487 samples = 104 value = [93, 11] 600->670 602 sex_Male <= 0.5 entropy = 0.451 samples = 53 value = [48, 5] 601->602 625 workclass_Public <= 0.5 entropy = 0.784 samples = 60 value = [46, 14] 601->625 603 age <= 30.5 entropy = 0.216 samples = 29 value = [28, 1] 602->603 608 workclass_Private <= 0.5 entropy = 0.65 samples = 24 value = [20, 4] 602->608 604 workclass_Private <= 0.5 entropy = 0.439 samples = 11 value = [10, 1] 603->604 607 entropy = 0.0 samples = 18 value = [18, 0] 603->607 605 entropy = 0.0 samples = 5 value = [5, 0] 604->605 606 entropy = 0.65 samples = 6 value = [5, 1] 604->606 609 age <= 30.5 entropy = 0.971 samples = 5 value = [3, 2] 608->609 614 race_White <= 0.5 entropy = 0.485 samples = 19 value = [17, 2] 608->614 610 entropy = 0.0 samples = 1 value = [0, 1] 609->610 611 race_Black <= 0.5 entropy = 0.811 samples = 4 value = [3, 1] 609->611 612 entropy = 0.0 samples = 3 value = [3, 0] 611->612 613 entropy = 0.0 samples = 1 value = [0, 1] 611->613 615 entropy = 0.0 samples = 5 value = [5, 0] 614->615 616 hours-per-week <= 39.0 entropy = 0.592 samples = 14 value = [12, 2] 614->616 617 entropy = 0.0 samples = 2 value = [2, 0] 616->617 618 age <= 31.5 entropy = 0.65 samples = 12 value = [10, 2] 616->618 619 age <= 30.5 entropy = 0.918 samples = 3 value = [2, 1] 618->619 622 age <= 32.5 entropy = 0.503 samples = 9 value = [8, 1] 618->622 620 entropy = 0.0 samples = 1 value = [1, 0] 619->620 621 entropy = 1.0 samples = 2 value = [1, 1] 619->621 623 entropy = 0.0 samples = 5 value = [5, 0] 622->623 624 entropy = 0.811 samples = 4 value = [3, 1] 622->624 626 education <= 13.5 entropy = 0.863 samples = 42 value = [30, 12] 625->626 661 hours-per-week <= 39.0 entropy = 0.503 samples = 18 value = [16, 2] 625->661 627 hours-per-week <= 41.0 entropy = 0.822 samples = 35 value = [26, 9] 626->627 654 race_Asian <= 0.5 entropy = 0.985 samples = 7 value = [4, 3] 626->654 628 hours-per-week <= 33.5 entropy = 0.834 samples = 34 value = [25, 9] 627->628 653 entropy = 0.0 samples = 1 value = [1, 0] 627->653 629 entropy = 1.0 samples = 2 value = [1, 1] 628->629 630 hours-per-week <= 39.0 entropy = 0.811 samples = 32 value = [24, 8] 628->630 631 entropy = 0.0 samples = 4 value = [4, 0] 630->631 632 sex_Female <= 0.5 entropy = 0.863 samples = 28 value = [20, 8] 630->632 633 workclass_Self-emp <= 0.5 entropy = 0.991 samples = 9 value = [5, 4] 632->633 642 workclass_Private <= 0.5 entropy = 0.742 samples = 19 value = [15, 4] 632->642 634 age <= 36.5 entropy = 0.918 samples = 6 value = [2, 4] 633->634 641 entropy = 0.0 samples = 3 value = [3, 0] 633->641 635 age <= 34.5 entropy = 0.971 samples = 5 value = [2, 3] 634->635 640 entropy = 0.0 samples = 1 value = [0, 1] 634->640 636 entropy = 1.0 samples = 2 value = [1, 1] 635->636 637 age <= 35.5 entropy = 0.918 samples = 3 value = [1, 2] 635->637 638 entropy = 0.0 samples = 1 value = [0, 1] 637->638 639 entropy = 1.0 samples = 2 value = [1, 1] 637->639 643 entropy = 0.0 samples = 1 value = [0, 1] 642->643 644 age <= 35.5 entropy = 0.65 samples = 18 value = [15, 3] 642->644 645 race_Black <= 0.5 entropy = 0.845 samples = 11 value = [8, 3] 644->645 652 entropy = 0.0 samples = 7 value = [7, 0] 644->652 646 race_White <= 0.5 entropy = 0.881 samples = 10 value = [7, 3] 645->646 651 entropy = 0.0 samples = 1 value = [1, 0] 645->651 647 entropy = 1.0 samples = 2 value = [1, 1] 646->647 648 age <= 34.5 entropy = 0.811 samples = 8 value = [6, 2] 646->648 649 entropy = 0.918 samples = 3 value = [2, 1] 648->649 650 entropy = 0.722 samples = 5 value = [4, 1] 648->650 655 age <= 35.5 entropy = 0.971 samples = 5 value = [2, 3] 654->655 660 entropy = 0.0 samples = 2 value = [2, 0] 654->660 656 entropy = 0.0 samples = 2 value = [0, 2] 655->656 657 sex_Female <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 655->657 658 entropy = 1.0 samples = 2 value = [1, 1] 657->658 659 entropy = 0.0 samples = 1 value = [1, 0] 657->659 662 entropy = 0.0 samples = 5 value = [5, 0] 661->662 663 age <= 36.5 entropy = 0.619 samples = 13 value = [11, 2] 661->663 664 sex_Male <= 0.5 entropy = 0.439 samples = 11 value = [10, 1] 663->664 669 entropy = 1.0 samples = 2 value = [1, 1] 663->669 665 entropy = 0.0 samples = 7 value = [7, 0] 664->665 666 education <= 13.5 entropy = 0.811 samples = 4 value = [3, 1] 664->666 667 entropy = 0.0 samples = 2 value = [2, 0] 666->667 668 entropy = 1.0 samples = 2 value = [1, 1] 666->668 671 entropy = 0.0 samples = 19 value = [19, 0] 670->671 672 workclass_Self-emp <= 0.5 entropy = 0.556 samples = 85 value = [74, 11] 670->672 673 hours-per-week <= 38.5 entropy = 0.592 samples = 77 value = [66, 11] 672->673 718 entropy = 0.0 samples = 8 value = [8, 0] 672->718 674 age <= 40.5 entropy = 0.811 samples = 12 value = [9, 3] 673->674 687 age <= 42.5 entropy = 0.538 samples = 65 value = [57, 8] 673->687 675 entropy = 0.0 samples = 4 value = [4, 0] 674->675 676 age <= 42.5 entropy = 0.954 samples = 8 value = [5, 3] 674->676 677 education <= 13.5 entropy = 1.0 samples = 6 value = [3, 3] 676->677 686 entropy = 0.0 samples = 2 value = [2, 0] 676->686 678 entropy = 0.0 samples = 1 value = [0, 1] 677->678 679 sex_Male <= 0.5 entropy = 0.971 samples = 5 value = [3, 2] 677->679 680 hours-per-week <= 37.0 entropy = 1.0 samples = 4 value = [2, 2] 679->680 685 entropy = 0.0 samples = 1 value = [1, 0] 679->685 681 workclass_Public <= 0.5 entropy = 0.918 samples = 3 value = [1, 2] 680->681 684 entropy = 0.0 samples = 1 value = [1, 0] 680->684 682 entropy = 0.0 samples = 1 value = [0, 1] 681->682 683 entropy = 1.0 samples = 2 value = [1, 1] 681->683 688 age <= 40.5 entropy = 0.391 samples = 39 value = [36, 3] 687->688 703 sex_Male <= 0.5 entropy = 0.706 samples = 26 value = [21, 5] 687->703 689 education <= 13.5 entropy = 0.696 samples = 16 value = [13, 3] 688->689 702 entropy = 0.0 samples = 23 value = [23, 0] 688->702 690 race_White <= 0.5 entropy = 0.811 samples = 12 value = [9, 3] 689->690 701 entropy = 0.0 samples = 4 value = [4, 0] 689->701 691 entropy = 0.0 samples = 1 value = [1, 0] 690->691 692 sex_Male <= 0.5 entropy = 0.845 samples = 11 value = [8, 3] 690->692 693 workclass_Private <= 0.5 entropy = 0.722 samples = 5 value = [4, 1] 692->693 698 workclass_Public <= 0.5 entropy = 0.918 samples = 6 value = [4, 2] 692->698 694 entropy = 0.0 samples = 2 value = [2, 0] 693->694 695 age <= 39.5 entropy = 0.918 samples = 3 value = [2, 1] 693->695 696 entropy = 0.0 samples = 1 value = [1, 0] 695->696 697 entropy = 1.0 samples = 2 value = [1, 1] 695->697 699 entropy = 0.811 samples = 4 value = [3, 1] 698->699 700 entropy = 1.0 samples = 2 value = [1, 1] 698->700 704 race_Black <= 0.5 entropy = 0.414 samples = 12 value = [11, 1] 703->704 707 race_White <= 0.5 entropy = 0.863 samples = 14 value = [10, 4] 703->707 705 entropy = 0.0 samples = 10 value = [10, 0] 704->705 706 entropy = 1.0 samples = 2 value = [1, 1] 704->706 708 entropy = 0.0 samples = 2 value = [2, 0] 707->708 709 education <= 13.5 entropy = 0.918 samples = 12 value = [8, 4] 707->709 710 age <= 43.5 entropy = 0.971 samples = 10 value = [6, 4] 709->710 717 entropy = 0.0 samples = 2 value = [2, 0] 709->717 711 workclass_Public <= 0.5 entropy = 1.0 samples = 6 value = [3, 3] 710->711 714 workclass_Private <= 0.5 entropy = 0.811 samples = 4 value = [3, 1] 710->714 712 entropy = 0.811 samples = 4 value = [3, 1] 711->712 713 entropy = 0.0 samples = 2 value = [0, 2] 711->713 715 entropy = 0.0 samples = 2 value = [2, 0] 714->715 716 entropy = 1.0 samples = 2 value = [1, 1] 714->716 720 age <= 74.5 entropy = 0.431 samples = 34 value = [31, 3] 719->720 731 workclass_Private <= 0.5 entropy = 0.931 samples = 78 value = [51, 27] 719->731 721 hours-per-week <= 33.5 entropy = 0.33 samples = 33 value = [31, 2] 720->721 730 entropy = 0.0 samples = 1 value = [0, 1] 720->730 722 entropy = 0.0 samples = 20 value = [20, 0] 721->722 723 education <= 13.5 entropy = 0.619 samples = 13 value = [11, 2] 721->723 724 entropy = 0.0 samples = 5 value = [5, 0] 723->724 725 age <= 46.5 entropy = 0.811 samples = 8 value = [6, 2] 723->725 726 entropy = 0.0 samples = 1 value = [0, 1] 725->726 727 age <= 51.5 entropy = 0.592 samples = 7 value = [6, 1] 725->727 728 entropy = 0.0 samples = 5 value = [5, 0] 727->728 729 entropy = 1.0 samples = 2 value = [1, 1] 727->729 732 hours-per-week <= 41.0 entropy = 0.985 samples = 35 value = [20, 15] 731->732 767 age <= 60.5 entropy = 0.854 samples = 43 value = [31, 12] 731->767 733 age <= 45.5 entropy = 0.977 samples = 34 value = [20, 14] 732->733 766 entropy = 0.0 samples = 1 value = [0, 1] 732->766 734 entropy = 0.0 samples = 4 value = [4, 0] 733->734 735 age <= 55.0 entropy = 0.997 samples = 30 value = [16, 14] 733->735 736 age <= 52.0 entropy = 0.994 samples = 22 value = [10, 12] 735->736 759 age <= 61.0 entropy = 0.811 samples = 8 value = [6, 2] 735->759 737 age <= 50.5 entropy = 1.0 samples = 20 value = [10, 10] 736->737 758 entropy = 0.0 samples = 2 value = [0, 2] 736->758 738 age <= 49.5 entropy = 0.998 samples = 19 value = [9, 10] 737->738 757 entropy = 0.0 samples = 1 value = [1, 0] 737->757 739 age <= 48.5 entropy = 0.997 samples = 15 value = [8, 7] 738->739 754 sex_Female <= 0.5 entropy = 0.811 samples = 4 value = [1, 3] 738->754 740 age <= 47.5 entropy = 0.971 samples = 10 value = [4, 6] 739->740 749 race_Black <= 0.5 entropy = 0.722 samples = 5 value = [4, 1] 739->749 741 education <= 13.5 entropy = 1.0 samples = 8 value = [4, 4] 740->741 748 entropy = 0.0 samples = 2 value = [0, 2] 740->748 742 race_White <= 0.5 entropy = 0.918 samples = 6 value = [2, 4] 741->742 747 entropy = 0.0 samples = 2 value = [2, 0] 741->747 743 entropy = 0.0 samples = 1 value = [1, 0] 742->743 744 age <= 46.5 entropy = 0.722 samples = 5 value = [1, 4] 742->744 745 entropy = 0.918 samples = 3 value = [1, 2] 744->745 746 entropy = 0.0 samples = 2 value = [0, 2] 744->746 750 entropy = 0.0 samples = 2 value = [2, 0] 749->750 751 education <= 13.5 entropy = 0.918 samples = 3 value = [2, 1] 749->751 752 entropy = 0.0 samples = 2 value = [2, 0] 751->752 753 entropy = 0.0 samples = 1 value = [0, 1] 751->753 755 entropy = 0.0 samples = 2 value = [0, 2] 754->755 756 entropy = 1.0 samples = 2 value = [1, 1] 754->756 760 entropy = 0.0 samples = 4 value = [4, 0] 759->760 761 education <= 13.5 entropy = 1.0 samples = 4 value = [2, 2] 759->761 762 workclass_Public <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 761->762 765 entropy = 0.0 samples = 1 value = [0, 1] 761->765 763 entropy = 1.0 samples = 2 value = [1, 1] 762->763 764 entropy = 0.0 samples = 1 value = [1, 0] 762->764 768 age <= 58.5 entropy = 0.8 samples = 37 value = [28, 9] 767->768 799 age <= 62.0 entropy = 1.0 samples = 6 value = [3, 3] 767->799 769 age <= 57.5 entropy = 0.834 samples = 34 value = [25, 9] 768->769 798 entropy = 0.0 samples = 3 value = [3, 0] 768->798 770 sex_Male <= 0.5 entropy = 0.799 samples = 33 value = [25, 8] 769->770 797 entropy = 0.0 samples = 1 value = [0, 1] 769->797 771 education <= 13.5 entropy = 0.523 samples = 17 value = [15, 2] 770->771 778 age <= 46.5 entropy = 0.954 samples = 16 value = [10, 6] 770->778 772 entropy = 0.0 samples = 10 value = [10, 0] 771->772 773 age <= 50.0 entropy = 0.863 samples = 7 value = [5, 2] 771->773 774 race_White <= 0.5 entropy = 0.918 samples = 3 value = [1, 2] 773->774 777 entropy = 0.0 samples = 4 value = [4, 0] 773->777 775 entropy = 0.0 samples = 1 value = [1, 0] 774->775 776 entropy = 0.0 samples = 2 value = [0, 2] 774->776 779 age <= 45.5 entropy = 0.722 samples = 5 value = [4, 1] 778->779 782 hours-per-week <= 41.0 entropy = 0.994 samples = 11 value = [6, 5] 778->782 780 entropy = 0.918 samples = 3 value = [2, 1] 779->780 781 entropy = 0.0 samples = 2 value = [2, 0] 779->781 783 education <= 13.5 entropy = 1.0 samples = 10 value = [5, 5] 782->783 796 entropy = 0.0 samples = 1 value = [1, 0] 782->796 784 age <= 52.5 entropy = 0.991 samples = 9 value = [5, 4] 783->784 795 entropy = 0.0 samples = 1 value = [0, 1] 783->795 785 age <= 51.5 entropy = 1.0 samples = 8 value = [4, 4] 784->785 794 entropy = 0.0 samples = 1 value = [1, 0] 784->794 786 age <= 50.5 entropy = 0.985 samples = 7 value = [4, 3] 785->786 793 entropy = 0.0 samples = 1 value = [0, 1] 785->793 787 race_Black <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 786->787 790 race_White <= 0.5 entropy = 1.0 samples = 4 value = [2, 2] 786->790 788 entropy = 1.0 samples = 2 value = [1, 1] 787->788 789 entropy = 0.0 samples = 1 value = [1, 0] 787->789 791 entropy = 0.0 samples = 1 value = [0, 1] 790->791 792 entropy = 0.918 samples = 3 value = [2, 1] 790->792 800 entropy = 0.0 samples = 1 value = [0, 1] 799->800 801 education <= 13.5 entropy = 0.971 samples = 5 value = [3, 2] 799->801 802 entropy = 0.0 samples = 2 value = [2, 0] 801->802 803 age <= 65.0 entropy = 0.918 samples = 3 value = [1, 2] 801->803 804 entropy = 0.0 samples = 2 value = [0, 2] 803->804 805 entropy = 0.0 samples = 1 value = [1, 0] 803->805 807 education <= 13.5 entropy = 0.925 samples = 188 value = [124, 64] 806->807 982 age <= 56.5 entropy = 0.99 samples = 43 value = [19, 24] 806->982 808 race_Hispanic <= 0.5 entropy = 0.881 samples = 130 value = [91, 39] 807->808 921 workclass_Public <= 0.5 entropy = 0.986 samples = 58 value = [33, 25] 807->921 809 age <= 43.5 entropy = 0.875 samples = 129 value = [91, 38] 808->809 920 entropy = 0.0 samples = 1 value = [0, 1] 808->920 810 hours-per-week <= 67.5 entropy = 0.823 samples = 97 value = [72, 25] 809->810 893 hours-per-week <= 47.0 entropy = 0.974 samples = 32 value = [19, 13] 809->893 811 age <= 38.225 entropy = 0.836 samples = 94 value = [69, 25] 810->811 892 entropy = 0.0 samples = 3 value = [3, 0] 810->892 812 age <= 36.5 entropy = 0.877 samples = 64 value = [45, 19] 811->812 867 hours-per-week <= 46.5 entropy = 0.722 samples = 30 value = [24, 6] 811->867 813 age <= 35.5 entropy = 0.838 samples = 56 value = [41, 15] 812->813 860 sex_Male <= 0.5 entropy = 1.0 samples = 8 value = [4, 4] 812->860 814 workclass_Self-emp <= 0.5 entropy = 0.879 samples = 47 value = [33, 14] 813->814 857 race_White <= 0.5 entropy = 0.503 samples = 9 value = [8, 1] 813->857 815 hours-per-week <= 49.0 entropy = 0.902 samples = 44 value = [30, 14] 814->815 856 entropy = 0.0 samples = 3 value = [3, 0] 814->856 816 age <= 30.5 entropy = 0.742 samples = 19 value = [15, 4] 815->816 831 race_Asian <= 0.5 entropy = 0.971 samples = 25 value = [15, 10] 815->831 817 hours-per-week <= 46.5 entropy = 1.0 samples = 4 value = [2, 2] 816->817 822 age <= 32.5 entropy = 0.567 samples = 15 value = [13, 2] 816->822 818 race_White <= 0.5 entropy = 0.918 samples = 3 value = [1, 2] 817->818 821 entropy = 0.0 samples = 1 value = [1, 0] 817->821 819 entropy = 1.0 samples = 2 value = [1, 1] 818->819 820 entropy = 0.0 samples = 1 value = [0, 1] 818->820 823 entropy = 0.0 samples = 8 value = [8, 0] 822->823 824 workclass_Private <= 0.5 entropy = 0.863 samples = 7 value = [5, 2] 822->824 825 entropy = 0.0 samples = 2 value = [2, 0] 824->825 826 age <= 34.5 entropy = 0.971 samples = 5 value = [3, 2] 824->826 827 age <= 33.5 entropy = 0.811 samples = 4 value = [3, 1] 826->827 830 entropy = 0.0 samples = 1 value = [0, 1] 826->830 828 entropy = 1.0 samples = 2 value = [1, 1] 827->828 829 entropy = 0.0 samples = 2 value = [2, 0] 827->829 832 sex_Female <= 0.5 entropy = 0.954 samples = 24 value = [15, 9] 831->832 855 entropy = 0.0 samples = 1 value = [0, 1] 831->855 833 race_Black <= 0.5 entropy = 0.896 samples = 16 value = [11, 5] 832->833 844 race_Black <= 0.5 entropy = 1.0 samples = 8 value = [4, 4] 832->844 834 age <= 34.5 entropy = 0.837 samples = 15 value = [11, 4] 833->834 843 entropy = 0.0 samples = 1 value = [0, 1] 833->843 835 age <= 31.5 entropy = 0.722 samples = 10 value = [8, 2] 834->835 842 entropy = 0.971 samples = 5 value = [3, 2] 834->842 836 workclass_Private <= 0.5 entropy = 0.863 samples = 7 value = [5, 2] 835->836 841 entropy = 0.0 samples = 3 value = [3, 0] 835->841 837 entropy = 0.0 samples = 1 value = [0, 1] 836->837 838 hours-per-week <= 52.5 entropy = 0.65 samples = 6 value = [5, 1] 836->838 839 entropy = 0.0 samples = 4 value = [4, 0] 838->839 840 entropy = 1.0 samples = 2 value = [1, 1] 838->840 845 age <= 30.5 entropy = 0.985 samples = 7 value = [3, 4] 844->845 854 entropy = 0.0 samples = 1 value = [1, 0] 844->854 846 entropy = 0.0 samples = 1 value = [1, 0] 845->846 847 age <= 34.0 entropy = 0.918 samples = 6 value = [2, 4] 845->847 848 age <= 31.5 entropy = 0.722 samples = 5 value = [1, 4] 847->848 853 entropy = 0.0 samples = 1 value = [1, 0] 847->853 849 workclass_Public <= 0.5 entropy = 0.918 samples = 3 value = [1, 2] 848->849 852 entropy = 0.0 samples = 2 value = [0, 2] 848->852 850 entropy = 0.0 samples = 2 value = [0, 2] 849->850 851 entropy = 0.0 samples = 1 value = [1, 0] 849->851 858 entropy = 1.0 samples = 2 value = [1, 1] 857->858 859 entropy = 0.0 samples = 7 value = [7, 0] 857->859 861 hours-per-week <= 47.5 entropy = 0.811 samples = 4 value = [3, 1] 860->861 864 hours-per-week <= 46.5 entropy = 0.811 samples = 4 value = [1, 3] 860->864 862 entropy = 0.0 samples = 1 value = [0, 1] 861->862 863 entropy = 0.0 samples = 3 value = [3, 0] 861->863 865 entropy = 0.0 samples = 1 value = [1, 0] 864->865 866 entropy = 0.0 samples = 3 value = [0, 3] 864->866 868 workclass_Private <= 0.5 entropy = 0.991 samples = 9 value = [5, 4] 867->868 879 age <= 41.5 entropy = 0.454 samples = 21 value = [19, 2] 867->879 869 entropy = 0.0 samples = 1 value = [0, 1] 868->869 870 hours-per-week <= 44.5 entropy = 0.954 samples = 8 value = [5, 3] 868->870 871 entropy = 0.0 samples = 1 value = [0, 1] 870->871 872 age <= 39.5 entropy = 0.863 samples = 7 value = [5, 2] 870->872 873 entropy = 0.0 samples = 1 value = [1, 0] 872->873 874 sex_Male <= 0.5 entropy = 0.918 samples = 6 value = [4, 2] 872->874 875 entropy = 1.0 samples = 2 value = [1, 1] 874->875 876 age <= 42.5 entropy = 0.811 samples = 4 value = [3, 1] 874->876 877 entropy = 0.0 samples = 2 value = [2, 0] 876->877 878 entropy = 1.0 samples = 2 value = [1, 1] 876->878 880 age <= 39.5 entropy = 0.592 samples = 14 value = [12, 2] 879->880 891 entropy = 0.0 samples = 7 value = [7, 0] 879->891 881 entropy = 0.0 samples = 6 value = [6, 0] 880->881 882 workclass_Private <= 0.5 entropy = 0.811 samples = 8 value = [6, 2] 880->882 883 entropy = 0.0 samples = 2 value = [2, 0] 882->883 884 race_Asian <= 0.5 entropy = 0.918 samples = 6 value = [4, 2] 882->884 885 hours-per-week <= 52.5 entropy = 1.0 samples = 4 value = [2, 2] 884->885 890 entropy = 0.0 samples = 2 value = [2, 0] 884->890 886 entropy = 0.0 samples = 1 value = [0, 1] 885->886 887 sex_Male <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 885->887 888 entropy = 0.0 samples = 2 value = [2, 0] 887->888 889 entropy = 0.0 samples = 1 value = [0, 1] 887->889 894 sex_Female <= 0.5 entropy = 0.684 samples = 11 value = [9, 2] 893->894 901 race_White <= 0.5 entropy = 0.998 samples = 21 value = [10, 11] 893->901 895 race_White <= 0.5 entropy = 0.918 samples = 6 value = [4, 2] 894->895 900 entropy = 0.0 samples = 5 value = [5, 0] 894->900 896 entropy = 0.0 samples = 1 value = [0, 1] 895->896 897 age <= 46.0 entropy = 0.722 samples = 5 value = [4, 1] 895->897 898 entropy = 1.0 samples = 2 value = [1, 1] 897->898 899 entropy = 0.0 samples = 3 value = [3, 0] 897->899 902 entropy = 0.0 samples = 2 value = [2, 0] 901->902 903 sex_Male <= 0.5 entropy = 0.982 samples = 19 value = [8, 11] 901->903 904 workclass_Private <= 0.5 entropy = 0.98 samples = 12 value = [7, 5] 903->904 917 age <= 47.5 entropy = 0.592 samples = 7 value = [1, 6] 903->917 905 age <= 47.5 entropy = 0.863 samples = 7 value = [5, 2] 904->905 912 hours-per-week <= 55.5 entropy = 0.971 samples = 5 value = [2, 3] 904->912 906 workclass_Self-emp <= 0.5 entropy = 1.0 samples = 4 value = [2, 2] 905->906 911 entropy = 0.0 samples = 3 value = [3, 0] 905->911 907 hours-per-week <= 55.0 entropy = 0.918 samples = 3 value = [1, 2] 906->907 910 entropy = 0.0 samples = 1 value = [1, 0] 906->910 908 entropy = 0.0 samples = 2 value = [0, 2] 907->908 909 entropy = 0.0 samples = 1 value = [1, 0] 907->909 913 age <= 44.5 entropy = 0.918 samples = 3 value = [2, 1] 912->913 916 entropy = 0.0 samples = 2 value = [0, 2] 912->916 914 entropy = 0.0 samples = 1 value = [0, 1] 913->914 915 entropy = 0.0 samples = 2 value = [2, 0] 913->915 918 entropy = 0.0 samples = 5 value = [0, 5] 917->918 919 entropy = 1.0 samples = 2 value = [1, 1] 917->919 922 age <= 36.5 entropy = 0.995 samples = 35 value = [16, 19] 921->922 961 age <= 39.5 entropy = 0.828 samples = 23 value = [17, 6] 921->961 923 age <= 34.5 entropy = 0.764 samples = 9 value = [2, 7] 922->923 930 race_White <= 0.5 entropy = 0.996 samples = 26 value = [14, 12] 922->930 924 hours-per-week <= 45.5 entropy = 1.0 samples = 4 value = [2, 2] 923->924 929 entropy = 0.0 samples = 5 value = [0, 5] 923->929 925 entropy = 0.0 samples = 1 value = [0, 1] 924->925 926 sex_Female <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 924->926 927 entropy = 0.0 samples = 1 value = [1, 0] 926->927 928 entropy = 1.0 samples = 2 value = [1, 1] 926->928 931 entropy = 0.0 samples = 1 value = [0, 1] 930->931 932 hours-per-week <= 77.5 entropy = 0.99 samples = 25 value = [14, 11] 930->932 933 hours-per-week <= 62.5 entropy = 0.98 samples = 24 value = [14, 10] 932->933 960 entropy = 0.0 samples = 1 value = [0, 1] 932->960 934 age <= 38.725 entropy = 0.994 samples = 22 value = [12, 10] 933->934 959 entropy = 0.0 samples = 2 value = [2, 0] 933->959 935 workclass_Private <= 0.5 entropy = 0.918 samples = 6 value = [2, 4] 934->935 942 hours-per-week <= 46.5 entropy = 0.954 samples = 16 value = [10, 6] 934->942 936 entropy = 0.0 samples = 1 value = [0, 1] 935->936 937 hours-per-week <= 47.5 entropy = 0.971 samples = 5 value = [2, 3] 935->937 938 entropy = 1.0 samples = 2 value = [1, 1] 937->938 939 age <= 38.225 entropy = 0.918 samples = 3 value = [1, 2] 937->939 940 entropy = 0.0 samples = 2 value = [0, 2] 939->940 941 entropy = 0.0 samples = 1 value = [1, 0] 939->941 943 entropy = 0.0 samples = 3 value = [3, 0] 942->943 944 hours-per-week <= 55.0 entropy = 0.996 samples = 13 value = [7, 6] 942->944 945 age <= 44.5 entropy = 1.0 samples = 12 value = [6, 6] 944->945 958 entropy = 0.0 samples = 1 value = [1, 0] 944->958 946 age <= 42.5 entropy = 0.811 samples = 4 value = [3, 1] 945->946 949 sex_Male <= 0.5 entropy = 0.954 samples = 8 value = [3, 5] 945->949 947 entropy = 1.0 samples = 2 value = [1, 1] 946->947 948 entropy = 0.0 samples = 2 value = [2, 0] 946->948 950 workclass_Private <= 0.5 entropy = 1.0 samples = 6 value = [3, 3] 949->950 957 entropy = 0.0 samples = 2 value = [0, 2] 949->957 951 entropy = 0.0 samples = 1 value = [1, 0] 950->951 952 age <= 46.5 entropy = 0.971 samples = 5 value = [2, 3] 950->952 953 age <= 45.5 entropy = 1.0 samples = 4 value = [2, 2] 952->953 956 entropy = 0.0 samples = 1 value = [0, 1] 952->956 954 entropy = 1.0 samples = 2 value = [1, 1] 953->954 955 entropy = 1.0 samples = 2 value = [1, 1] 953->955 962 entropy = 0.0 samples = 6 value = [6, 0] 961->962 963 race_White <= 0.5 entropy = 0.937 samples = 17 value = [11, 6] 961->963 964 entropy = 0.0 samples = 1 value = [0, 1] 963->964 965 hours-per-week <= 53.5 entropy = 0.896 samples = 16 value = [11, 5] 963->965 966 age <= 46.0 entropy = 0.544 samples = 8 value = [7, 1] 965->966 971 age <= 40.5 entropy = 1.0 samples = 8 value = [4, 4] 965->971 967 entropy = 0.0 samples = 4 value = [4, 0] 966->967 968 age <= 47.5 entropy = 0.811 samples = 4 value = [3, 1] 966->968 969 entropy = 1.0 samples = 2 value = [1, 1] 968->969 970 entropy = 0.0 samples = 2 value = [2, 0] 968->970 972 entropy = 0.0 samples = 1 value = [0, 1] 971->972 973 age <= 41.5 entropy = 0.985 samples = 7 value = [4, 3] 971->973 974 entropy = 0.0 samples = 2 value = [2, 0] 973->974 975 age <= 46.5 entropy = 0.971 samples = 5 value = [2, 3] 973->975 976 age <= 43.5 entropy = 1.0 samples = 4 value = [2, 2] 975->976 981 entropy = 0.0 samples = 1 value = [0, 1] 975->981 977 sex_Male <= 0.5 entropy = 0.918 samples = 3 value = [1, 2] 976->977 980 entropy = 0.0 samples = 1 value = [1, 0] 976->980 978 entropy = 0.0 samples = 1 value = [0, 1] 977->978 979 entropy = 1.0 samples = 2 value = [1, 1] 977->979 983 race_Amer-Indian <= 0.5 entropy = 0.881 samples = 30 value = [9, 21] 982->983 1008 sex_Female <= 0.5 entropy = 0.779 samples = 13 value = [10, 3] 982->1008 984 hours-per-week <= 57.5 entropy = 0.811 samples = 28 value = [7, 21] 983->984 1007 entropy = 0.0 samples = 2 value = [2, 0] 983->1007 985 education <= 13.5 entropy = 0.503 samples = 18 value = [2, 16] 984->985 1002 workclass_Private <= 0.5 entropy = 1.0 samples = 10 value = [5, 5] 984->1002 986 hours-per-week <= 52.5 entropy = 0.592 samples = 14 value = [2, 12] 985->986 1001 entropy = 0.0 samples = 4 value = [0, 4] 985->1001 987 age <= 54.5 entropy = 0.684 samples = 11 value = [2, 9] 986->987 1000 entropy = 0.0 samples = 3 value = [0, 3] 986->1000 988 hours-per-week <= 44.0 entropy = 0.811 samples = 8 value = [2, 6] 987->988 999 entropy = 0.0 samples = 3 value = [0, 3] 987->999 989 entropy = 0.0 samples = 2 value = [0, 2] 988->989 990 age <= 50.5 entropy = 0.918 samples = 6 value = [2, 4] 988->990 991 entropy = 0.0 samples = 1 value = [0, 1] 990->991 992 race_Black <= 0.5 entropy = 0.971 samples = 5 value = [2, 3] 990->992 993 sex_Male <= 0.5 entropy = 1.0 samples = 4 value = [2, 2] 992->993 998 entropy = 0.0 samples = 1 value = [0, 1] 992->998 994 entropy = 0.0 samples = 1 value = [1, 0] 993->994 995 hours-per-week <= 47.5 entropy = 0.918 samples = 3 value = [1, 2] 993->995 996 entropy = 0.0 samples = 1 value = [1, 0] 995->996 997 entropy = 0.0 samples = 2 value = [0, 2] 995->997 1003 age <= 53.5 entropy = 0.65 samples = 6 value = [1, 5] 1002->1003 1006 entropy = 0.0 samples = 4 value = [4, 0] 1002->1006 1004 entropy = 0.0 samples = 4 value = [0, 4] 1003->1004 1005 entropy = 1.0 samples = 2 value = [1, 1] 1003->1005 1009 race_Black <= 0.5 entropy = 0.439 samples = 11 value = [10, 1] 1008->1009 1012 entropy = 0.0 samples = 2 value = [0, 2] 1008->1012 1010 entropy = 0.0 samples = 10 value = [10, 0] 1009->1010 1011 entropy = 0.0 samples = 1 value = [0, 1] 1009->1011 1014 age <= 52.5 entropy = 0.883 samples = 63 value = [19, 44] 1013->1014 1059 entropy = 0.0 samples = 3 value = [3, 0] 1013->1059 1015 hours-per-week <= 46.5 entropy = 0.811 samples = 56 value = [14, 42] 1014->1015 1054 age <= 59.0 entropy = 0.863 samples = 7 value = [5, 2] 1014->1054 1016 sex_Male <= 0.5 entropy = 0.958 samples = 29 value = [11, 18] 1015->1016 1041 sex_Male <= 0.5 entropy = 0.503 samples = 27 value = [3, 24] 1015->1041 1017 age <= 41.0 entropy = 0.811 samples = 8 value = [6, 2] 1016->1017 1024 age <= 32.0 entropy = 0.792 samples = 21 value = [5, 16] 1016->1024 1018 entropy = 0.0 samples = 3 value = [3, 0] 1017->1018 1019 workclass_Private <= 0.5 entropy = 0.971 samples = 5 value = [3, 2] 1017->1019 1020 age <= 43.0 entropy = 0.811 samples = 4 value = [3, 1] 1019->1020 1023 entropy = 0.0 samples = 1 value = [0, 1] 1019->1023 1021 entropy = 1.0 samples = 2 value = [1, 1] 1020->1021 1022 entropy = 0.0 samples = 2 value = [2, 0] 1020->1022 1025 entropy = 0.0 samples = 1 value = [1, 0] 1024->1025 1026 workclass_Public <= 0.5 entropy = 0.722 samples = 20 value = [4, 16] 1024->1026 1027 workclass_Self-emp <= 0.5 entropy = 0.523 samples = 17 value = [2, 15] 1026->1027 1038 age <= 40.0 entropy = 0.918 samples = 3 value = [2, 1] 1026->1038 1028 age <= 35.0 entropy = 0.65 samples = 12 value = [2, 10] 1027->1028 1037 entropy = 0.0 samples = 5 value = [0, 5] 1027->1037 1029 entropy = 0.0 samples = 2 value = [0, 2] 1028->1029 1030 age <= 49.0 entropy = 0.722 samples = 10 value = [2, 8] 1028->1030 1031 age <= 46.0 entropy = 0.811 samples = 8 value = [2, 6] 1030->1031 1036 entropy = 0.0 samples = 2 value = [0, 2] 1030->1036 1032 education <= 15.5 entropy = 0.592 samples = 7 value = [1, 6] 1031->1032 1035 entropy = 0.0 samples = 1 value = [1, 0] 1031->1035 1033 entropy = 0.0 samples = 5 value = [0, 5] 1032->1033 1034 entropy = 1.0 samples = 2 value = [1, 1] 1032->1034 1039 entropy = 0.0 samples = 1 value = [1, 0] 1038->1039 1040 entropy = 1.0 samples = 2 value = [1, 1] 1038->1040 1042 entropy = 0.0 samples = 13 value = [0, 13] 1041->1042 1043 education <= 15.5 entropy = 0.75 samples = 14 value = [3, 11] 1041->1043 1044 workclass_Self-emp <= 0.5 entropy = 0.881 samples = 10 value = [3, 7] 1043->1044 1053 entropy = 0.0 samples = 4 value = [0, 4] 1043->1053 1045 age <= 38.225 entropy = 0.954 samples = 8 value = [3, 5] 1044->1045 1052 entropy = 0.0 samples = 2 value = [0, 2] 1044->1052 1046 age <= 31.5 entropy = 0.918 samples = 3 value = [2, 1] 1045->1046 1049 workclass_Private <= 0.5 entropy = 0.722 samples = 5 value = [1, 4] 1045->1049 1047 entropy = 0.0 samples = 1 value = [0, 1] 1046->1047 1048 entropy = 0.0 samples = 2 value = [2, 0] 1046->1048 1050 entropy = 1.0 samples = 2 value = [1, 1] 1049->1050 1051 entropy = 0.0 samples = 3 value = [0, 3] 1049->1051 1055 entropy = 0.0 samples = 4 value = [4, 0] 1054->1055 1056 age <= 64.5 entropy = 0.918 samples = 3 value = [1, 2] 1054->1056 1057 entropy = 0.0 samples = 2 value = [0, 2] 1056->1057 1058 entropy = 0.0 samples = 1 value = [1, 0] 1056->1058 1061 education <= 8.5 entropy = 0.887 samples = 3222 value = [2241, 981] 1060->1061 2946 hours-per-week <= 41.5 entropy = 0.907 samples = 1515 value = [489, 1026] 1060->2946 1062 hours-per-week <= 39.5 entropy = 0.491 samples = 551 value = [492, 59] 1061->1062 1273 age <= 29.5 entropy = 0.93 samples = 2671 value = [1749, 922] 1061->1273 1063 sex_Female <= 0.5 entropy = 0.178 samples = 112 value = [109, 3] 1062->1063 1080 age <= 38.225 entropy = 0.551 samples = 439 value = [383, 56] 1062->1080 1064 education <= 7.5 entropy = 0.242 samples = 75 value = [72, 3] 1063->1064 1079 entropy = 0.0 samples = 37 value = [37, 0] 1063->1079 1065 age <= 76.5 entropy = 0.183 samples = 72 value = [70, 2] 1064->1065 1076 workclass_Public <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 1064->1076 1066 education <= 5.5 entropy = 0.111 samples = 68 value = [67, 1] 1065->1066 1073 age <= 78.0 entropy = 0.811 samples = 4 value = [3, 1] 1065->1073 1067 entropy = 0.0 samples = 44 value = [44, 0] 1066->1067 1068 age <= 53.5 entropy = 0.25 samples = 24 value = [23, 1] 1066->1068 1069 age <= 51.0 entropy = 0.439 samples = 11 value = [10, 1] 1068->1069 1072 entropy = 0.0 samples = 13 value = [13, 0] 1068->1072 1070 entropy = 0.0 samples = 10 value = [10, 0] 1069->1070 1071 entropy = 0.0 samples = 1 value = [0, 1] 1069->1071 1074 entropy = 0.0 samples = 1 value = [0, 1] 1073->1074 1075 entropy = 0.0 samples = 3 value = [3, 0] 1073->1075 1077 entropy = 0.0 samples = 2 value = [2, 0] 1076->1077 1078 entropy = 0.0 samples = 1 value = [0, 1] 1076->1078 1081 hours-per-week <= 77.5 entropy = 0.355 samples = 164 value = [153, 11] 1080->1081 1128 workclass_Self-emp <= 0.5 entropy = 0.643 samples = 275 value = [230, 45] 1080->1128 1082 workclass_Self-emp <= 0.5 entropy = 0.334 samples = 162 value = [152, 10] 1081->1082 1127 entropy = 1.0 samples = 2 value = [1, 1] 1081->1127 1083 age <= 28.5 entropy = 0.282 samples = 143 value = [136, 7] 1082->1083 1118 age <= 29.0 entropy = 0.629 samples = 19 value = [16, 3] 1082->1118 1084 entropy = 0.0 samples = 49 value = [49, 0] 1083->1084 1085 age <= 33.5 entropy = 0.382 samples = 94 value = [87, 7] 1083->1085 1086 education <= 5.5 entropy = 0.246 samples = 49 value = [47, 2] 1085->1086 1099 education <= 4.5 entropy = 0.503 samples = 45 value = [40, 5] 1085->1099 1087 entropy = 0.0 samples = 22 value = [22, 0] 1086->1087 1088 age <= 31.5 entropy = 0.381 samples = 27 value = [25, 2] 1086->1088 1089 education <= 7.5 entropy = 0.503 samples = 18 value = [16, 2] 1088->1089 1098 entropy = 0.0 samples = 9 value = [9, 0] 1088->1098 1090 age <= 30.5 entropy = 0.353 samples = 15 value = [14, 1] 1089->1090 1095 hours-per-week <= 45.0 entropy = 0.918 samples = 3 value = [2, 1] 1089->1095 1091 entropy = 0.0 samples = 10 value = [10, 0] 1090->1091 1092 education <= 6.5 entropy = 0.722 samples = 5 value = [4, 1] 1090->1092 1093 entropy = 0.918 samples = 3 value = [2, 1] 1092->1093 1094 entropy = 0.0 samples = 2 value = [2, 0] 1092->1094 1096 entropy = 1.0 samples = 2 value = [1, 1] 1095->1096 1097 entropy = 0.0 samples = 1 value = [1, 0] 1095->1097 1100 sex_Female <= 0.5 entropy = 0.75 samples = 14 value = [11, 3] 1099->1100 1111 hours-per-week <= 43.5 entropy = 0.345 samples = 31 value = [29, 2] 1099->1111 1101 age <= 36.5 entropy = 0.619 samples = 13 value = [11, 2] 1100->1101 1110 entropy = 0.0 samples = 1 value = [0, 1] 1100->1110 1102 hours-per-week <= 42.5 entropy = 0.863 samples = 7 value = [5, 2] 1101->1102 1109 entropy = 0.0 samples = 6 value = [6, 0] 1101->1109 1103 age <= 35.5 entropy = 0.918 samples = 6 value = [4, 2] 1102->1103 1108 entropy = 0.0 samples = 1 value = [1, 0] 1102->1108 1104 education <= 3.5 entropy = 0.811 samples = 4 value = [3, 1] 1103->1104 1107 entropy = 1.0 samples = 2 value = [1, 1] 1103->1107 1105 entropy = 0.0 samples = 1 value = [1, 0] 1104->1105 1106 entropy = 0.918 samples = 3 value = [2, 1] 1104->1106 1112 entropy = 0.0 samples = 17 value = [17, 0] 1111->1112 1113 hours-per-week <= 45.5 entropy = 0.592 samples = 14 value = [12, 2] 1111->1113 1114 age <= 37.5 entropy = 1.0 samples = 4 value = [2, 2] 1113->1114 1117 entropy = 0.0 samples = 10 value = [10, 0] 1113->1117 1115 entropy = 0.0 samples = 2 value = [0, 2] 1114->1115 1116 entropy = 0.0 samples = 2 value = [2, 0] 1114->1116 1119 age <= 27.5 entropy = 0.918 samples = 9 value = [6, 3] 1118->1119 1126 entropy = 0.0 samples = 10 value = [10, 0] 1118->1126 1120 age <= 25.0 entropy = 0.811 samples = 8 value = [6, 2] 1119->1120 1125 entropy = 0.0 samples = 1 value = [0, 1] 1119->1125 1121 age <= 22.5 entropy = 1.0 samples = 4 value = [2, 2] 1120->1121 1124 entropy = 0.0 samples = 4 value = [4, 0] 1120->1124 1122 entropy = 0.0 samples = 2 value = [2, 0] 1121->1122 1123 entropy = 0.0 samples = 2 value = [0, 2] 1121->1123 1129 age <= 41.5 entropy = 0.588 samples = 226 value = [194, 32] 1128->1129 1244 hours-per-week <= 59.5 entropy = 0.835 samples = 49 value = [36, 13] 1128->1244 1130 education <= 3.5 entropy = 0.834 samples = 34 value = [25, 9] 1129->1130 1159 education <= 4.5 entropy = 0.529 samples = 192 value = [169, 23] 1129->1159 1131 age <= 40.5 entropy = 0.469 samples = 10 value = [9, 1] 1130->1131 1136 age <= 40.5 entropy = 0.918 samples = 24 value = [16, 8] 1130->1136 1132 entropy = 0.0 samples = 4 value = [4, 0] 1131->1132 1133 education <= 2.5 entropy = 0.65 samples = 6 value = [5, 1] 1131->1133 1134 entropy = 0.918 samples = 3 value = [2, 1] 1133->1134 1135 entropy = 0.0 samples = 3 value = [3, 0] 1133->1135 1137 education <= 6.5 entropy = 0.811 samples = 20 value = [15, 5] 1136->1137 1154 race_Black <= 0.5 entropy = 0.811 samples = 4 value = [1, 3] 1136->1154 1138 hours-per-week <= 51.5 entropy = 0.98 samples = 12 value = [7, 5] 1137->1138 1153 entropy = 0.0 samples = 8 value = [8, 0] 1137->1153 1139 sex_Female <= 0.5 entropy = 0.946 samples = 11 value = [7, 4] 1138->1139 1152 entropy = 0.0 samples = 1 value = [0, 1] 1138->1152 1140 race_Black <= 0.5 entropy = 0.881 samples = 10 value = [7, 3] 1139->1140 1151 entropy = 0.0 samples = 1 value = [0, 1] 1139->1151 1141 age <= 39.5 entropy = 0.764 samples = 9 value = [7, 2] 1140->1141 1150 entropy = 0.0 samples = 1 value = [0, 1] 1140->1150 1142 entropy = 0.0 samples = 4 value = [4, 0] 1141->1142 1143 race_White <= 0.5 entropy = 0.971 samples = 5 value = [3, 2] 1141->1143 1144 entropy = 0.0 samples = 1 value = [1, 0] 1143->1144 1145 education <= 4.5 entropy = 1.0 samples = 4 value = [2, 2] 1143->1145 1146 entropy = 0.0 samples = 1 value = [0, 1] 1145->1146 1147 education <= 5.5 entropy = 0.918 samples = 3 value = [2, 1] 1145->1147 1148 entropy = 0.0 samples = 1 value = [1, 0] 1147->1148 1149 entropy = 1.0 samples = 2 value = [1, 1] 1147->1149 1155 education <= 7.5 entropy = 0.918 samples = 3 value = [1, 2] 1154->1155 1158 entropy = 0.0 samples = 1 value = [0, 1] 1154->1158 1156 entropy = 1.0 samples = 2 value = [1, 1] 1155->1156 1157 entropy = 0.0 samples = 1 value = [0, 1] 1155->1157 1160 hours-per-week <= 49.5 entropy = 0.316 samples = 70 value = [66, 4] 1159->1160 1177 education <= 6.5 entropy = 0.624 samples = 122 value = [103, 19] 1159->1177 1161 race_Asian <= 0.5 entropy = 0.127 samples = 57 value = [56, 1] 1160->1161 1166 education <= 3.5 entropy = 0.779 samples = 13 value = [10, 3] 1160->1166 1162 entropy = 0.0 samples = 54 value = [54, 0] 1161->1162 1163 sex_Male <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 1161->1163 1164 entropy = 0.0 samples = 1 value = [0, 1] 1163->1164 1165 entropy = 0.0 samples = 2 value = [2, 0] 1163->1165 1167 entropy = 0.0 samples = 5 value = [5, 0] 1166->1167 1168 age <= 47.5 entropy = 0.954 samples = 8 value = [5, 3] 1166->1168 1169 entropy = 0.0 samples = 1 value = [0, 1] 1168->1169 1170 age <= 52.0 entropy = 0.863 samples = 7 value = [5, 2] 1168->1170 1171 entropy = 0.0 samples = 2 value = [2, 0] 1170->1171 1172 age <= 54.0 entropy = 0.971 samples = 5 value = [3, 2] 1170->1172 1173 entropy = 0.0 samples = 1 value = [0, 1] 1172->1173 1174 age <= 58.0 entropy = 0.811 samples = 4 value = [3, 1] 1172->1174 1175 entropy = 0.0 samples = 2 value = [2, 0] 1174->1175 1176 entropy = 1.0 samples = 2 value = [1, 1] 1174->1176 1178 age <= 63.5 entropy = 0.748 samples = 75 value = [59, 16] 1177->1178 1233 age <= 49.5 entropy = 0.342 samples = 47 value = [44, 3] 1177->1233 1179 age <= 59.5 entropy = 0.698 samples = 69 value = [56, 13] 1178->1179 1224 hours-per-week <= 59.0 entropy = 1.0 samples = 6 value = [3, 3] 1178->1224 1180 workclass_Public <= 0.5 entropy = 0.775 samples = 57 value = [44, 13] 1179->1180 1223 entropy = 0.0 samples = 12 value = [12, 0] 1179->1223 1181 sex_Female <= 0.5 entropy = 0.827 samples = 50 value = [37, 13] 1180->1181 1222 entropy = 0.0 samples = 7 value = [7, 0] 1180->1222 1182 age <= 57.5 entropy = 0.876 samples = 44 value = [31, 13] 1181->1182 1221 entropy = 0.0 samples = 6 value = [6, 0] 1181->1221 1183 race_Black <= 0.5 entropy = 0.79 samples = 38 value = [29, 9] 1182->1183 1214 education <= 5.5 entropy = 0.918 samples = 6 value = [2, 4] 1182->1214 1184 age <= 54.5 entropy = 0.684 samples = 33 value = [27, 6] 1183->1184 1209 hours-per-week <= 43.5 entropy = 0.971 samples = 5 value = [2, 3] 1183->1209 1185 age <= 50.0 entropy = 0.764 samples = 27 value = [21, 6] 1184->1185 1208 entropy = 0.0 samples = 6 value = [6, 0] 1184->1208 1186 education <= 5.5 entropy = 0.503 samples = 18 value = [16, 2] 1185->1186 1197 age <= 53.5 entropy = 0.991 samples = 9 value = [5, 4] 1185->1197 1187 entropy = 0.0 samples = 7 value = [7, 0] 1186->1187 1188 hours-per-week <= 41.0 entropy = 0.684 samples = 11 value = [9, 2] 1186->1188 1189 age <= 43.5 entropy = 0.918 samples = 6 value = [4, 2] 1188->1189 1196 entropy = 0.0 samples = 5 value = [5, 0] 1188->1196 1190 entropy = 0.0 samples = 1 value = [1, 0] 1189->1190 1191 age <= 44.5 entropy = 0.971 samples = 5 value = [3, 2] 1189->1191 1192 entropy = 1.0 samples = 2 value = [1, 1] 1191->1192 1193 age <= 46.0 entropy = 0.918 samples = 3 value = [2, 1] 1191->1193 1194 entropy = 0.0 samples = 1 value = [1, 0] 1193->1194 1195 entropy = 1.0 samples = 2 value = [1, 1] 1193->1195 1198 age <= 52.5 entropy = 0.954 samples = 8 value = [5, 3] 1197->1198 1207 entropy = 0.0 samples = 1 value = [0, 1] 1197->1207 1199 hours-per-week <= 46.0 entropy = 1.0 samples = 6 value = [3, 3] 1198->1199 1206 entropy = 0.0 samples = 2 value = [2, 0] 1198->1206 1200 education <= 5.5 entropy = 0.811 samples = 4 value = [3, 1] 1199->1200 1205 entropy = 0.0 samples = 2 value = [0, 2] 1199->1205 1201 entropy = 0.0 samples = 1 value = [1, 0] 1200->1201 1202 hours-per-week <= 42.5 entropy = 0.918 samples = 3 value = [2, 1] 1200->1202 1203 entropy = 1.0 samples = 2 value = [1, 1] 1202->1203 1204 entropy = 0.0 samples = 1 value = [1, 0] 1202->1204 1210 age <= 55.5 entropy = 0.811 samples = 4 value = [1, 3] 1209->1210 1213 entropy = 0.0 samples = 1 value = [1, 0] 1209->1213 1211 entropy = 0.0 samples = 3 value = [0, 3] 1210->1211 1212 entropy = 0.0 samples = 1 value = [1, 0] 1210->1212 1215 entropy = 0.0 samples = 1 value = [0, 1] 1214->1215 1216 hours-per-week <= 45.0 entropy = 0.971 samples = 5 value = [2, 3] 1214->1216 1217 age <= 58.5 entropy = 0.811 samples = 4 value = [1, 3] 1216->1217 1220 entropy = 0.0 samples = 1 value = [1, 0] 1216->1220 1218 entropy = 0.0 samples = 1 value = [0, 1] 1217->1218 1219 entropy = 0.918 samples = 3 value = [1, 2] 1217->1219 1225 sex_Female <= 0.5 entropy = 0.971 samples = 5 value = [2, 3] 1224->1225 1232 entropy = 0.0 samples = 1 value = [1, 0] 1224->1232 1226 age <= 65.0 entropy = 1.0 samples = 4 value = [2, 2] 1225->1226 1231 entropy = 0.0 samples = 1 value = [0, 1] 1225->1231 1227 education <= 5.5 entropy = 0.918 samples = 3 value = [1, 2] 1226->1227 1230 entropy = 0.0 samples = 1 value = [1, 0] 1226->1230 1228 entropy = 0.0 samples = 1 value = [1, 0] 1227->1228 1229 entropy = 0.0 samples = 2 value = [0, 2] 1227->1229 1234 entropy = 0.0 samples = 20 value = [20, 0] 1233->1234 1235 workclass_Public <= 0.5 entropy = 0.503 samples = 27 value = [24, 3] 1233->1235 1236 hours-per-week <= 61.0 entropy = 0.391 samples = 26 value = [24, 2] 1235->1236 1243 entropy = 0.0 samples = 1 value = [0, 1] 1235->1243 1237 age <= 59.5 entropy = 0.242 samples = 25 value = [24, 1] 1236->1237 1242 entropy = 0.0 samples = 1 value = [0, 1] 1236->1242 1238 entropy = 0.0 samples = 16 value = [16, 0] 1237->1238 1239 age <= 61.5 entropy = 0.503 samples = 9 value = [8, 1] 1237->1239 1240 entropy = 1.0 samples = 2 value = [1, 1] 1239->1240 1241 entropy = 0.0 samples = 7 value = [7, 0] 1239->1241 1245 education <= 6.5 entropy = 0.952 samples = 35 value = [22, 13] 1244->1245 1272 entropy = 0.0 samples = 14 value = [14, 0] 1244->1272 1246 hours-per-week <= 49.0 entropy = 0.869 samples = 31 value = [22, 9] 1245->1246 1271 entropy = 0.0 samples = 4 value = [0, 4] 1245->1271 1247 education <= 4.5 entropy = 0.667 samples = 23 value = [19, 4] 1246->1247 1262 age <= 64.0 entropy = 0.954 samples = 8 value = [3, 5] 1246->1262 1248 age <= 44.5 entropy = 0.918 samples = 12 value = [8, 4] 1247->1248 1261 entropy = 0.0 samples = 11 value = [11, 0] 1247->1261 1249 entropy = 0.0 samples = 3 value = [3, 0] 1248->1249 1250 education <= 3.5 entropy = 0.991 samples = 9 value = [5, 4] 1248->1250 1251 entropy = 0.0 samples = 2 value = [0, 2] 1250->1251 1252 age <= 53.0 entropy = 0.863 samples = 7 value = [5, 2] 1250->1252 1253 entropy = 0.0 samples = 2 value = [2, 0] 1252->1253 1254 age <= 56.5 entropy = 0.971 samples = 5 value = [3, 2] 1252->1254 1255 entropy = 0.0 samples = 1 value = [0, 1] 1254->1255 1256 age <= 59.5 entropy = 0.811 samples = 4 value = [3, 1] 1254->1256 1257 entropy = 0.0 samples = 1 value = [1, 0] 1256->1257 1258 age <= 63.5 entropy = 0.918 samples = 3 value = [2, 1] 1256->1258 1259 entropy = 1.0 samples = 2 value = [1, 1] 1258->1259 1260 entropy = 0.0 samples = 1 value = [1, 0] 1258->1260 1263 age <= 55.5 entropy = 0.863 samples = 7 value = [2, 5] 1262->1263 1270 entropy = 0.0 samples = 1 value = [1, 0] 1262->1270 1264 hours-per-week <= 52.5 entropy = 0.971 samples = 5 value = [2, 3] 1263->1264 1269 entropy = 0.0 samples = 2 value = [0, 2] 1263->1269 1265 education <= 3.5 entropy = 0.918 samples = 3 value = [2, 1] 1264->1265 1268 entropy = 0.0 samples = 2 value = [0, 2] 1264->1268 1266 entropy = 0.0 samples = 1 value = [1, 0] 1265->1266 1267 entropy = 1.0 samples = 2 value = [1, 1] 1265->1267 1274 age <= 23.5 entropy = 0.59 samples = 345 value = [296, 49] 1273->1274 1417 hours-per-week <= 34.5 entropy = 0.955 samples = 2326 value = [1453, 873] 1273->1417 1275 entropy = 0.0 samples = 68 value = [68, 0] 1274->1275 1276 hours-per-week <= 64.0 entropy = 0.673 samples = 277 value = [228, 49] 1274->1276 1277 age <= 28.5 entropy = 0.643 samples = 263 value = [220, 43] 1276->1277 1408 age <= 25.5 entropy = 0.985 samples = 14 value = [8, 6] 1276->1408 1278 race_Black <= 0.5 entropy = 0.588 samples = 198 value = [170, 28] 1277->1278 1369 race_Asian <= 0.5 entropy = 0.779 samples = 65 value = [50, 15] 1277->1369 1279 hours-per-week <= 41.0 entropy = 0.615 samples = 184 value = [156, 28] 1278->1279 1368 entropy = 0.0 samples = 14 value = [14, 0] 1278->1368 1280 race_Asian <= 0.5 entropy = 0.52 samples = 120 value = [106, 14] 1279->1280 1337 hours-per-week <= 44.5 entropy = 0.758 samples = 64 value = [50, 14] 1279->1337 1281 workclass_Public <= 0.5 entropy = 0.483 samples = 115 value = [103, 12] 1280->1281 1332 sex_Female <= 0.5 entropy = 0.971 samples = 5 value = [3, 2] 1280->1332 1282 education <= 9.5 entropy = 0.51 samples = 106 value = [94, 12] 1281->1282 1331 entropy = 0.0 samples = 9 value = [9, 0] 1281->1331 1283 age <= 24.5 entropy = 0.404 samples = 62 value = [57, 5] 1282->1283 1308 hours-per-week <= 35.5 entropy = 0.632 samples = 44 value = [37, 7] 1282->1308 1284 entropy = 0.0 samples = 10 value = [10, 0] 1283->1284 1285 hours-per-week <= 28.0 entropy = 0.457 samples = 52 value = [47, 5] 1283->1285 1286 hours-per-week <= 25.5 entropy = 0.918 samples = 3 value = [2, 1] 1285->1286 1289 workclass_Private <= 0.5 entropy = 0.408 samples = 49 value = [45, 4] 1285->1289 1287 entropy = 0.0 samples = 2 value = [2, 0] 1286->1287 1288 entropy = 0.0 samples = 1 value = [0, 1] 1286->1288 1290 age <= 26.0 entropy = 0.722 samples = 5 value = [4, 1] 1289->1290 1295 age <= 25.5 entropy = 0.359 samples = 44 value = [41, 3] 1289->1295 1291 hours-per-week <= 35.0 entropy = 0.918 samples = 3 value = [2, 1] 1290->1291 1294 entropy = 0.0 samples = 2 value = [2, 0] 1290->1294 1292 entropy = 0.0 samples = 1 value = [1, 0] 1291->1292 1293 entropy = 1.0 samples = 2 value = [1, 1] 1291->1293 1296 entropy = 0.0 samples = 11 value = [11, 0] 1295->1296 1297 sex_Female <= 0.5 entropy = 0.439 samples = 33 value = [30, 3] 1295->1297 1298 age <= 26.5 entropy = 0.371 samples = 28 value = [26, 2] 1297->1298 1305 age <= 26.5 entropy = 0.722 samples = 5 value = [4, 1] 1297->1305 1299 entropy = 0.0 samples = 4 value = [4, 0] 1298->1299 1300 hours-per-week <= 37.5 entropy = 0.414 samples = 24 value = [22, 2] 1298->1300 1301 entropy = 0.0 samples = 1 value = [1, 0] 1300->1301 1302 age <= 27.5 entropy = 0.426 samples = 23 value = [21, 2] 1300->1302 1303 entropy = 0.503 samples = 9 value = [8, 1] 1302->1303 1304 entropy = 0.371 samples = 14 value = [13, 1] 1302->1304 1306 entropy = 1.0 samples = 2 value = [1, 1] 1305->1306 1307 entropy = 0.0 samples = 3 value = [3, 0] 1305->1307 1309 entropy = 0.0 samples = 8 value = [8, 0] 1308->1309 1310 hours-per-week <= 37.0 entropy = 0.711 samples = 36 value = [29, 7] 1308->1310 1311 entropy = 0.0 samples = 1 value = [0, 1] 1310->1311 1312 age <= 26.5 entropy = 0.661 samples = 35 value = [29, 6] 1310->1312 1313 sex_Female <= 0.5 entropy = 0.469 samples = 20 value = [18, 2] 1312->1313 1320 education <= 10.5 entropy = 0.837 samples = 15 value = [11, 4] 1312->1320 1314 age <= 24.5 entropy = 0.523 samples = 17 value = [15, 2] 1313->1314 1319 entropy = 0.0 samples = 3 value = [3, 0] 1313->1319 1315 entropy = 0.811 samples = 4 value = [3, 1] 1314->1315 1316 age <= 25.5 entropy = 0.391 samples = 13 value = [12, 1] 1314->1316 1317 entropy = 0.0 samples = 4 value = [4, 0] 1316->1317 1318 entropy = 0.503 samples = 9 value = [8, 1] 1316->1318 1321 age <= 27.5 entropy = 0.918 samples = 12 value = [8, 4] 1320->1321 1330 entropy = 0.0 samples = 3 value = [3, 0] 1320->1330 1322 sex_Female <= 0.5 entropy = 0.811 samples = 4 value = [1, 3] 1321->1322 1325 sex_Female <= 0.5 entropy = 0.544 samples = 8 value = [7, 1] 1321->1325 1323 entropy = 0.918 samples = 3 value = [1, 2] 1322->1323 1324 entropy = 0.0 samples = 1 value = [0, 1] 1322->1324 1326 workclass_Private <= 0.5 entropy = 0.65 samples = 6 value = [5, 1] 1325->1326 1329 entropy = 0.0 samples = 2 value = [2, 0] 1325->1329 1327 entropy = 0.0 samples = 1 value = [1, 0] 1326->1327 1328 entropy = 0.722 samples = 5 value = [4, 1] 1326->1328 1333 hours-per-week <= 30.0 entropy = 0.918 samples = 3 value = [1, 2] 1332->1333 1336 entropy = 0.0 samples = 2 value = [2, 0] 1332->1336 1334 entropy = 0.0 samples = 1 value = [1, 0] 1333->1334 1335 entropy = 0.0 samples = 2 value = [0, 2] 1333->1335 1338 age <= 24.5 entropy = 0.985 samples = 7 value = [3, 4] 1337->1338 1345 hours-per-week <= 51.5 entropy = 0.67 samples = 57 value = [47, 10] 1337->1345 1339 entropy = 0.0 samples = 1 value = [1, 0] 1338->1339 1340 age <= 25.5 entropy = 0.918 samples = 6 value = [2, 4] 1338->1340 1341 entropy = 0.0 samples = 3 value = [0, 3] 1340->1341 1342 age <= 27.0 entropy = 0.918 samples = 3 value = [2, 1] 1340->1342 1343 entropy = 0.0 samples = 2 value = [2, 0] 1342->1343 1344 entropy = 0.0 samples = 1 value = [0, 1] 1342->1344 1346 hours-per-week <= 47.0 entropy = 0.769 samples = 40 value = [31, 9] 1345->1346 1365 age <= 24.5 entropy = 0.323 samples = 17 value = [16, 1] 1345->1365 1347 age <= 24.5 entropy = 0.353 samples = 15 value = [14, 1] 1346->1347 1352 sex_Female <= 0.5 entropy = 0.904 samples = 25 value = [17, 8] 1346->1352 1348 workclass_Public <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 1347->1348 1351 entropy = 0.0 samples = 12 value = [12, 0] 1347->1351 1349 entropy = 1.0 samples = 2 value = [1, 1] 1348->1349 1350 entropy = 0.0 samples = 1 value = [1, 0] 1348->1350 1353 age <= 27.5 entropy = 0.828 samples = 23 value = [17, 6] 1352->1353 1364 entropy = 0.0 samples = 2 value = [0, 2] 1352->1364 1354 age <= 26.5 entropy = 0.523 samples = 17 value = [15, 2] 1353->1354 1361 hours-per-week <= 49.0 entropy = 0.918 samples = 6 value = [2, 4] 1353->1361 1355 age <= 25.5 entropy = 0.764 samples = 9 value = [7, 2] 1354->1355 1360 entropy = 0.0 samples = 8 value = [8, 0] 1354->1360 1356 entropy = 0.0 samples = 5 value = [5, 0] 1355->1356 1357 education <= 9.5 entropy = 1.0 samples = 4 value = [2, 2] 1355->1357 1358 entropy = 0.0 samples = 1 value = [0, 1] 1357->1358 1359 entropy = 0.918 samples = 3 value = [2, 1] 1357->1359 1362 entropy = 0.0 samples = 2 value = [2, 0] 1361->1362 1363 entropy = 0.0 samples = 4 value = [0, 4] 1361->1363 1366 entropy = 1.0 samples = 2 value = [1, 1] 1365->1366 1367 entropy = 0.0 samples = 15 value = [15, 0] 1365->1367 1370 workclass_Self-emp <= 0.5 entropy = 0.798 samples = 62 value = [47, 15] 1369->1370 1407 entropy = 0.0 samples = 3 value = [3, 0] 1369->1407 1371 hours-per-week <= 39.0 entropy = 0.757 samples = 55 value = [43, 12] 1370->1371 1400 race_Amer-Indian <= 0.5 entropy = 0.985 samples = 7 value = [4, 3] 1370->1400 1372 entropy = 0.0 samples = 5 value = [5, 0] 1371->1372 1373 sex_Female <= 0.5 entropy = 0.795 samples = 50 value = [38, 12] 1371->1373 1374 education <= 10.5 entropy = 0.755 samples = 46 value = [36, 10] 1373->1374 1399 entropy = 1.0 samples = 4 value = [2, 2] 1373->1399 1375 race_Hispanic <= 0.5 entropy = 0.79 samples = 38 value = [29, 9] 1374->1375 1396 hours-per-week <= 44.0 entropy = 0.544 samples = 8 value = [7, 1] 1374->1396 1376 hours-per-week <= 57.5 entropy = 0.8 samples = 37 value = [28, 9] 1375->1376 1395 entropy = 0.0 samples = 1 value = [1, 0] 1375->1395 1377 hours-per-week <= 49.0 entropy = 0.811 samples = 36 value = [27, 9] 1376->1377 1394 entropy = 0.0 samples = 1 value = [1, 0] 1376->1394 1378 education <= 9.5 entropy = 0.771 samples = 31 value = [24, 7] 1377->1378 1389 education <= 9.5 entropy = 0.971 samples = 5 value = [3, 2] 1377->1389 1379 hours-per-week <= 42.5 entropy = 0.65 samples = 24 value = [20, 4] 1378->1379 1384 race_Black <= 0.5 entropy = 0.985 samples = 7 value = [4, 3] 1378->1384 1380 race_Black <= 0.5 entropy = 0.722 samples = 20 value = [16, 4] 1379->1380 1383 entropy = 0.0 samples = 4 value = [4, 0] 1379->1383 1381 entropy = 0.764 samples = 18 value = [14, 4] 1380->1381 1382 entropy = 0.0 samples = 2 value = [2, 0] 1380->1382 1385 hours-per-week <= 42.5 entropy = 0.918 samples = 6 value = [4, 2] 1384->1385 1388 entropy = 0.0 samples = 1 value = [0, 1] 1384->1388 1386 entropy = 0.811 samples = 4 value = [3, 1] 1385->1386 1387 entropy = 1.0 samples = 2 value = [1, 1] 1385->1387 1390 hours-per-week <= 52.5 entropy = 0.918 samples = 3 value = [1, 2] 1389->1390 1393 entropy = 0.0 samples = 2 value = [2, 0] 1389->1393 1391 entropy = 0.0 samples = 1 value = [0, 1] 1390->1391 1392 entropy = 1.0 samples = 2 value = [1, 1] 1390->1392 1397 entropy = 0.722 samples = 5 value = [4, 1] 1396->1397 1398 entropy = 0.0 samples = 3 value = [3, 0] 1396->1398 1401 sex_Male <= 0.5 entropy = 0.918 samples = 6 value = [4, 2] 1400->1401 1406 entropy = 0.0 samples = 1 value = [0, 1] 1400->1406 1402 entropy = 0.0 samples = 1 value = [0, 1] 1401->1402 1403 hours-per-week <= 47.5 entropy = 0.722 samples = 5 value = [4, 1] 1401->1403 1404 entropy = 0.918 samples = 3 value = [2, 1] 1403->1404 1405 entropy = 0.0 samples = 2 value = [2, 0] 1403->1405 1409 education <= 10.5 entropy = 0.722 samples = 5 value = [1, 4] 1408->1409 1412 race_Black <= 0.5 entropy = 0.764 samples = 9 value = [7, 2] 1408->1412 1410 entropy = 0.0 samples = 4 value = [0, 4] 1409->1410 1411 entropy = 0.0 samples = 1 value = [1, 0] 1409->1411 1413 hours-per-week <= 67.5 entropy = 0.544 samples = 8 value = [7, 1] 1412->1413 1416 entropy = 0.0 samples = 1 value = [0, 1] 1412->1416 1414 entropy = 1.0 samples = 2 value = [1, 1] 1413->1414 1415 entropy = 0.0 samples = 6 value = [6, 0] 1413->1415 1418 education <= 9.5 entropy = 0.55 samples = 228 value = [199, 29] 1417->1418 1507 age <= 35.5 entropy = 0.972 samples = 2098 value = [1254, 844] 1417->1507 1419 hours-per-week <= 15.5 entropy = 0.336 samples = 145 value = [136, 9] 1418->1419 1458 age <= 61.5 entropy = 0.797 samples = 83 value = [63, 20] 1418->1458 1420 entropy = 0.0 samples = 26 value = [26, 0] 1419->1420 1421 sex_Female <= 0.5 entropy = 0.387 samples = 119 value = [110, 9] 1419->1421 1422 age <= 57.5 entropy = 0.206 samples = 62 value = [60, 2] 1421->1422 1429 race_White <= 0.5 entropy = 0.537 samples = 57 value = [50, 7] 1421->1429 1423 entropy = 0.0 samples = 31 value = [31, 0] 1422->1423 1424 age <= 59.5 entropy = 0.345 samples = 31 value = [29, 2] 1422->1424 1425 hours-per-week <= 24.5 entropy = 0.971 samples = 5 value = [3, 2] 1424->1425 1428 entropy = 0.0 samples = 26 value = [26, 0] 1424->1428 1426 entropy = 0.0 samples = 2 value = [0, 2] 1425->1426 1427 entropy = 0.0 samples = 3 value = [3, 0] 1425->1427 1430 entropy = 0.0 samples = 10 value = [10, 0] 1429->1430 1431 hours-per-week <= 21.0 entropy = 0.607 samples = 47 value = [40, 7] 1429->1431 1432 workclass_Private <= 0.5 entropy = 0.831 samples = 19 value = [14, 5] 1431->1432 1447 hours-per-week <= 28.5 entropy = 0.371 samples = 28 value = [26, 2] 1431->1447 1433 age <= 44.0 entropy = 0.985 samples = 7 value = [4, 3] 1432->1433 1440 age <= 47.5 entropy = 0.65 samples = 12 value = [10, 2] 1432->1440 1434 entropy = 0.0 samples = 1 value = [1, 0] 1433->1434 1435 age <= 62.5 entropy = 1.0 samples = 6 value = [3, 3] 1433->1435 1436 entropy = 0.0 samples = 2 value = [0, 2] 1435->1436 1437 age <= 70.5 entropy = 0.811 samples = 4 value = [3, 1] 1435->1437 1438 entropy = 0.0 samples = 2 value = [2, 0] 1437->1438 1439 entropy = 1.0 samples = 2 value = [1, 1] 1437->1439 1441 age <= 44.0 entropy = 0.863 samples = 7 value = [5, 2] 1440->1441 1446 entropy = 0.0 samples = 5 value = [5, 0] 1440->1446 1442 hours-per-week <= 18.0 entropy = 0.65 samples = 6 value = [5, 1] 1441->1442 1445 entropy = 0.0 samples = 1 value = [0, 1] 1441->1445 1443 entropy = 0.0 samples = 1 value = [0, 1] 1442->1443 1444 entropy = 0.0 samples = 5 value = [5, 0] 1442->1444 1448 entropy = 0.0 samples = 14 value = [14, 0] 1447->1448 1449 hours-per-week <= 31.0 entropy = 0.592 samples = 14 value = [12, 2] 1447->1449 1450 age <= 38.5 entropy = 0.764 samples = 9 value = [7, 2] 1449->1450 1457 entropy = 0.0 samples = 5 value = [5, 0] 1449->1457 1451 entropy = 1.0 samples = 2 value = [1, 1] 1450->1451 1452 age <= 54.5 entropy = 0.592 samples = 7 value = [6, 1] 1450->1452 1453 entropy = 0.0 samples = 4 value = [4, 0] 1452->1453 1454 age <= 61.5 entropy = 0.918 samples = 3 value = [2, 1] 1452->1454 1455 entropy = 1.0 samples = 2 value = [1, 1] 1454->1455 1456 entropy = 0.0 samples = 1 value = [1, 0] 1454->1456 1459 race_White <= 0.5 entropy = 0.905 samples = 53 value = [36, 17] 1458->1459 1494 workclass_Self-emp <= 0.5 entropy = 0.469 samples = 30 value = [27, 3] 1458->1494 1460 entropy = 0.0 samples = 6 value = [6, 0] 1459->1460 1461 workclass_Private <= 0.5 entropy = 0.944 samples = 47 value = [30, 17] 1459->1461 1462 hours-per-week <= 22.0 entropy = 0.65 samples = 18 value = [15, 3] 1461->1462 1471 age <= 32.5 entropy = 0.999 samples = 29 value = [15, 14] 1461->1471 1463 age <= 33.5 entropy = 0.918 samples = 9 value = [6, 3] 1462->1463 1470 entropy = 0.0 samples = 9 value = [9, 0] 1462->1470 1464 entropy = 0.0 samples = 1 value = [0, 1] 1463->1464 1465 age <= 57.0 entropy = 0.811 samples = 8 value = [6, 2] 1463->1465 1466 age <= 38.0 entropy = 0.592 samples = 7 value = [6, 1] 1465->1466 1469 entropy = 0.0 samples = 1 value = [0, 1] 1465->1469 1467 entropy = 1.0 samples = 2 value = [1, 1] 1466->1467 1468 entropy = 0.0 samples = 5 value = [5, 0] 1466->1468 1472 entropy = 0.0 samples = 4 value = [4, 0] 1471->1472 1473 hours-per-week <= 33.0 entropy = 0.99 samples = 25 value = [11, 14] 1471->1473 1474 age <= 58.5 entropy = 0.98 samples = 24 value = [10, 14] 1473->1474 1493 entropy = 0.0 samples = 1 value = [1, 0] 1473->1493 1475 hours-per-week <= 15.5 entropy = 0.994 samples = 22 value = [10, 12] 1474->1475 1492 entropy = 0.0 samples = 2 value = [0, 2] 1474->1492 1476 hours-per-week <= 8.0 entropy = 0.722 samples = 5 value = [4, 1] 1475->1476 1479 age <= 53.0 entropy = 0.937 samples = 17 value = [6, 11] 1475->1479 1477 entropy = 0.0 samples = 1 value = [0, 1] 1476->1477 1478 entropy = 0.0 samples = 4 value = [4, 0] 1476->1478 1480 age <= 41.5 entropy = 0.779 samples = 13 value = [3, 10] 1479->1480 1489 sex_Female <= 0.5 entropy = 0.811 samples = 4 value = [3, 1] 1479->1489 1481 hours-per-week <= 31.0 entropy = 0.985 samples = 7 value = [3, 4] 1480->1481 1488 entropy = 0.0 samples = 6 value = [0, 6] 1480->1488 1482 hours-per-week <= 24.5 entropy = 0.971 samples = 5 value = [3, 2] 1481->1482 1487 entropy = 0.0 samples = 2 value = [0, 2] 1481->1487 1483 hours-per-week <= 18.0 entropy = 0.918 samples = 3 value = [1, 2] 1482->1483 1486 entropy = 0.0 samples = 2 value = [2, 0] 1482->1486 1484 entropy = 0.0 samples = 1 value = [1, 0] 1483->1484 1485 entropy = 0.0 samples = 2 value = [0, 2] 1483->1485 1490 entropy = 1.0 samples = 2 value = [1, 1] 1489->1490 1491 entropy = 0.0 samples = 2 value = [2, 0] 1489->1491 1495 entropy = 0.0 samples = 19 value = [19, 0] 1494->1495 1496 age <= 62.5 entropy = 0.845 samples = 11 value = [8, 3] 1494->1496 1497 entropy = 0.0 samples = 2 value = [2, 0] 1496->1497 1498 hours-per-week <= 12.5 entropy = 0.918 samples = 9 value = [6, 3] 1496->1498 1499 education <= 10.5 entropy = 1.0 samples = 4 value = [2, 2] 1498->1499 1504 age <= 71.0 entropy = 0.722 samples = 5 value = [4, 1] 1498->1504 1500 hours-per-week <= 9.0 entropy = 0.918 samples = 3 value = [1, 2] 1499->1500 1503 entropy = 0.0 samples = 1 value = [1, 0] 1499->1503 1501 entropy = 0.0 samples = 1 value = [1, 0] 1500->1501 1502 entropy = 0.0 samples = 2 value = [0, 2] 1500->1502 1505 entropy = 0.0 samples = 4 value = [4, 0] 1504->1505 1506 entropy = 0.0 samples = 1 value = [0, 1] 1504->1506 1508 hours-per-week <= 47.0 entropy = 0.894 samples = 431 value = [297, 134] 1507->1508 1791 age <= 62.5 entropy = 0.984 samples = 1667 value = [957, 710] 1507->1791 1509 race_White <= 0.5 entropy = 0.808 samples = 282 value = [212, 70] 1508->1509 1654 sex_Male <= 0.5 entropy = 0.986 samples = 149 value = [85, 64] 1508->1654 1510 race_Amer-Indian <= 0.5 entropy = 0.384 samples = 40 value = [37, 3] 1509->1510 1523 sex_Male <= 0.5 entropy = 0.851 samples = 242 value = [175, 67] 1509->1523 1511 education <= 9.5 entropy = 0.303 samples = 37 value = [35, 2] 1510->1511 1520 education <= 9.5 entropy = 0.918 samples = 3 value = [2, 1] 1510->1520 1512 entropy = 0.0 samples = 26 value = [26, 0] 1511->1512 1513 age <= 32.0 entropy = 0.684 samples = 11 value = [9, 2] 1511->1513 1514 entropy = 0.0 samples = 7 value = [7, 0] 1513->1514 1515 race_Asian <= 0.5 entropy = 1.0 samples = 4 value = [2, 2] 1513->1515 1516 sex_Male <= 0.5 entropy = 0.918 samples = 3 value = [1, 2] 1515->1516 1519 entropy = 0.0 samples = 1 value = [1, 0] 1515->1519 1517 entropy = 0.0 samples = 1 value = [0, 1] 1516->1517 1518 entropy = 1.0 samples = 2 value = [1, 1] 1516->1518 1521 entropy = 0.0 samples = 1 value = [0, 1] 1520->1521 1522 entropy = 0.0 samples = 2 value = [2, 0] 1520->1522 1524 hours-per-week <= 43.0 entropy = 0.958 samples = 29 value = [18, 11] 1523->1524 1553 education <= 10.5 entropy = 0.831 samples = 213 value = [157, 56] 1523->1553 1525 education <= 10.5 entropy = 0.983 samples = 26 value = [15, 11] 1524->1525 1552 entropy = 0.0 samples = 3 value = [3, 0] 1524->1552 1526 age <= 30.5 entropy = 0.932 samples = 23 value = [15, 8] 1525->1526 1551 entropy = 0.0 samples = 3 value = [0, 3] 1525->1551 1527 entropy = 0.0 samples = 3 value = [3, 0] 1526->1527 1528 workclass_Public <= 0.5 entropy = 0.971 samples = 20 value = [12, 8] 1526->1528 1529 age <= 32.5 entropy = 0.949 samples = 19 value = [12, 7] 1528->1529 1550 entropy = 0.0 samples = 1 value = [0, 1] 1528->1550 1530 age <= 31.5 entropy = 0.592 samples = 7 value = [6, 1] 1529->1530 1535 age <= 33.5 entropy = 1.0 samples = 12 value = [6, 6] 1529->1535 1531 education <= 9.5 entropy = 0.811 samples = 4 value = [3, 1] 1530->1531 1534 entropy = 0.0 samples = 3 value = [3, 0] 1530->1534 1532 entropy = 0.918 samples = 3 value = [2, 1] 1531->1532 1533 entropy = 0.0 samples = 1 value = [1, 0] 1531->1533 1536 hours-per-week <= 37.5 entropy = 0.811 samples = 4 value = [1, 3] 1535->1536 1541 hours-per-week <= 39.0 entropy = 0.954 samples = 8 value = [5, 3] 1535->1541 1537 entropy = 0.0 samples = 1 value = [0, 1] 1536->1537 1538 hours-per-week <= 41.0 entropy = 0.918 samples = 3 value = [1, 2] 1536->1538 1539 entropy = 1.0 samples = 2 value = [1, 1] 1538->1539 1540 entropy = 0.0 samples = 1 value = [0, 1] 1538->1540 1542 entropy = 0.0 samples = 1 value = [1, 0] 1541->1542 1543 workclass_Self-emp <= 0.5 entropy = 0.985 samples = 7 value = [4, 3] 1541->1543 1544 age <= 34.5 entropy = 1.0 samples = 6 value = [3, 3] 1543->1544 1549 entropy = 0.0 samples = 1 value = [1, 0] 1543->1549 1545 education <= 9.5 entropy = 0.971 samples = 5 value = [3, 2] 1544->1545 1548 entropy = 0.0 samples = 1 value = [0, 1] 1544->1548 1546 entropy = 0.811 samples = 4 value = [3, 1] 1545->1546 1547 entropy = 0.0 samples = 1 value = [0, 1] 1545->1547 1554 hours-per-week <= 45.5 entropy = 0.849 samples = 196 value = [142, 54] 1553->1554 1641 age <= 30.5 entropy = 0.523 samples = 17 value = [15, 2] 1553->1641 1555 hours-per-week <= 37.0 entropy = 0.853 samples = 194 value = [140, 54] 1554->1555 1640 entropy = 0.0 samples = 2 value = [2, 0] 1554->1640 1556 age <= 33.5 entropy = 0.544 samples = 8 value = [7, 1] 1555->1556 1561 age <= 33.5 entropy = 0.862 samples = 186 value = [133, 53] 1555->1561 1557 entropy = 0.0 samples = 4 value = [4, 0] 1556->1557 1558 age <= 34.5 entropy = 0.811 samples = 4 value = [3, 1] 1556->1558 1559 entropy = 0.0 samples = 1 value = [0, 1] 1558->1559 1560 entropy = 0.0 samples = 3 value = [3, 0] 1558->1560 1562 hours-per-week <= 38.5 entropy = 0.894 samples = 119 value = [82, 37] 1561->1562 1609 workclass_Private <= 0.5 entropy = 0.793 samples = 67 value = [51, 16] 1561->1609 1563 entropy = 0.0 samples = 1 value = [0, 1] 1562->1563 1564 workclass_Public <= 0.5 entropy = 0.887 samples = 118 value = [82, 36] 1562->1564 1565 hours-per-week <= 39.5 entropy = 0.908 samples = 105 value = [71, 34] 1564->1565 1602 age <= 32.5 entropy = 0.619 samples = 13 value = [11, 2] 1564->1602 1566 entropy = 0.0 samples = 1 value = [1, 0] 1565->1566 1567 hours-per-week <= 40.5 entropy = 0.912 samples = 104 value = [70, 34] 1565->1567 1568 age <= 30.5 entropy = 0.899 samples = 92 value = [63, 29] 1567->1568 1591 hours-per-week <= 42.0 entropy = 0.98 samples = 12 value = [7, 5] 1567->1591 1569 education <= 9.5 entropy = 0.855 samples = 25 value = [18, 7] 1568->1569 1574 education <= 9.5 entropy = 0.913 samples = 67 value = [45, 22] 1568->1574 1570 entropy = 0.764 samples = 18 value = [14, 4] 1569->1570 1571 workclass_Private <= 0.5 entropy = 0.985 samples = 7 value = [4, 3] 1569->1571 1572 entropy = 0.0 samples = 1 value = [1, 0] 1571->1572 1573 entropy = 1.0 samples = 6 value = [3, 3] 1571->1573 1575 workclass_Private <= 0.5 entropy = 0.925 samples = 47 value = [31, 16] 1574->1575 1584 workclass_Private <= 0.5 entropy = 0.881 samples = 20 value = [14, 6] 1574->1584 1576 age <= 31.5 entropy = 0.985 samples = 7 value = [4, 3] 1575->1576 1579 age <= 32.5 entropy = 0.91 samples = 40 value = [27, 13] 1575->1579 1577 entropy = 1.0 samples = 2 value = [1, 1] 1576->1577 1578 entropy = 0.971 samples = 5 value = [3, 2] 1576->1578 1580 age <= 31.5 entropy = 0.918 samples = 27 value = [18, 9] 1579->1580 1583 entropy = 0.89 samples = 13 value = [9, 4] 1579->1583 1581 entropy = 0.918 samples = 12 value = [8, 4] 1580->1581 1582 entropy = 0.918 samples = 15 value = [10, 5] 1580->1582 1585 entropy = 0.0 samples = 3 value = [3, 0] 1584->1585 1586 age <= 31.5 entropy = 0.937 samples = 17 value = [11, 6] 1584->1586 1587 entropy = 0.881 samples = 10 value = [7, 3] 1586->1587 1588 age <= 32.5 entropy = 0.985 samples = 7 value = [4, 3] 1586->1588 1589 entropy = 0.971 samples = 5 value = [3, 2] 1588->1589 1590 entropy = 1.0 samples = 2 value = [1, 1] 1588->1590 1592 entropy = 0.0 samples = 1 value = [0, 1] 1591->1592 1593 education <= 9.5 entropy = 0.946 samples = 11 value = [7, 4] 1591->1593 1594 age <= 32.0 entropy = 0.985 samples = 7 value = [3, 4] 1593->1594 1601 entropy = 0.0 samples = 4 value = [4, 0] 1593->1601 1595 hours-per-week <= 44.0 entropy = 0.811 samples = 4 value = [1, 3] 1594->1595 1598 hours-per-week <= 44.0 entropy = 0.918 samples = 3 value = [2, 1] 1594->1598 1596 entropy = 0.0 samples = 1 value = [0, 1] 1595->1596 1597 entropy = 0.918 samples = 3 value = [1, 2] 1595->1597 1599 entropy = 0.0 samples = 1 value = [1, 0] 1598->1599 1600 entropy = 1.0 samples = 2 value = [1, 1] 1598->1600 1603 age <= 30.5 entropy = 0.439 samples = 11 value = [10, 1] 1602->1603 1608 entropy = 1.0 samples = 2 value = [1, 1] 1602->1608 1604 education <= 9.5 entropy = 0.918 samples = 3 value = [2, 1] 1603->1604 1607 entropy = 0.0 samples = 8 value = [8, 0] 1603->1607 1605 entropy = 1.0 samples = 2 value = [1, 1] 1604->1605 1606 entropy = 0.0 samples = 1 value = [1, 0] 1604->1606 1610 hours-per-week <= 42.5 entropy = 0.954 samples = 16 value = [10, 6] 1609->1610 1625 education <= 9.5 entropy = 0.714 samples = 51 value = [41, 10] 1609->1625 1611 education <= 9.5 entropy = 0.985 samples = 14 value = [8, 6] 1610->1611 1624 entropy = 0.0 samples = 2 value = [2, 0] 1610->1624 1612 age <= 34.5 entropy = 0.991 samples = 9 value = [4, 5] 1611->1612 1619 age <= 34.5 entropy = 0.722 samples = 5 value = [4, 1] 1611->1619 1613 workclass_Self-emp <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 1612->1613 1616 workclass_Public <= 0.5 entropy = 0.918 samples = 6 value = [2, 4] 1612->1616 1614 entropy = 0.0 samples = 1 value = [1, 0] 1613->1614 1615 entropy = 1.0 samples = 2 value = [1, 1] 1613->1615 1617 entropy = 1.0 samples = 2 value = [1, 1] 1616->1617 1618 entropy = 0.811 samples = 4 value = [1, 3] 1616->1618 1620 hours-per-week <= 39.0 entropy = 0.918 samples = 3 value = [2, 1] 1619->1620 1623 entropy = 0.0 samples = 2 value = [2, 0] 1619->1623 1621 entropy = 0.0 samples = 1 value = [1, 0] 1620->1621 1622 entropy = 1.0 samples = 2 value = [1, 1] 1620->1622 1626 hours-per-week <= 43.5 entropy = 0.614 samples = 33 value = [28, 5] 1625->1626 1635 age <= 34.5 entropy = 0.852 samples = 18 value = [13, 5] 1625->1635 1627 age <= 34.5 entropy = 0.491 samples = 28 value = [25, 3] 1626->1627 1632 hours-per-week <= 44.5 entropy = 0.971 samples = 5 value = [3, 2] 1626->1632 1628 entropy = 0.0 samples = 14 value = [14, 0] 1627->1628 1629 hours-per-week <= 41.5 entropy = 0.75 samples = 14 value = [11, 3] 1627->1629 1630 entropy = 0.779 samples = 13 value = [10, 3] 1629->1630 1631 entropy = 0.0 samples = 1 value = [1, 0] 1629->1631 1633 entropy = 0.0 samples = 1 value = [0, 1] 1632->1633 1634 entropy = 0.811 samples = 4 value = [3, 1] 1632->1634 1636 entropy = 1.0 samples = 8 value = [4, 4] 1635->1636 1637 hours-per-week <= 42.5 entropy = 0.469 samples = 10 value = [9, 1] 1635->1637 1638 entropy = 0.503 samples = 9 value = [8, 1] 1637->1638 1639 entropy = 0.0 samples = 1 value = [1, 0] 1637->1639 1642 entropy = 0.0 samples = 4 value = [4, 0] 1641->1642 1643 hours-per-week <= 41.0 entropy = 0.619 samples = 13 value = [11, 2] 1641->1643 1644 age <= 34.5 entropy = 0.764 samples = 9 value = [7, 2] 1643->1644 1653 entropy = 0.0 samples = 4 value = [4, 0] 1643->1653 1645 workclass_Public <= 0.5 entropy = 0.863 samples = 7 value = [5, 2] 1644->1645 1652 entropy = 0.0 samples = 2 value = [2, 0] 1644->1652 1646 age <= 31.5 entropy = 0.918 samples = 6 value = [4, 2] 1645->1646 1651 entropy = 0.0 samples = 1 value = [1, 0] 1645->1651 1647 entropy = 1.0 samples = 2 value = [1, 1] 1646->1647 1648 age <= 33.0 entropy = 0.811 samples = 4 value = [3, 1] 1646->1648 1649 entropy = 0.0 samples = 1 value = [1, 0] 1648->1649 1650 entropy = 0.918 samples = 3 value = [2, 1] 1648->1650 1655 age <= 30.5 entropy = 0.544 samples = 8 value = [7, 1] 1654->1655 1658 age <= 32.5 entropy = 0.992 samples = 141 value = [78, 63] 1654->1658 1656 entropy = 0.0 samples = 1 value = [0, 1] 1655->1656 1657 entropy = 0.0 samples = 7 value = [7, 0] 1655->1657 1659 hours-per-week <= 54.5 entropy = 0.954 samples = 64 value = [40, 24] 1658->1659 1722 hours-per-week <= 49.0 entropy = 1.0 samples = 77 value = [38, 39] 1658->1722 1660 workclass_Private <= 0.5 entropy = 0.758 samples = 32 value = [25, 7] 1659->1660 1685 hours-per-week <= 85.0 entropy = 0.997 samples = 32 value = [15, 17] 1659->1685 1661 entropy = 0.0 samples = 6 value = [6, 0] 1660->1661 1662 hours-per-week <= 51.5 entropy = 0.84 samples = 26 value = [19, 7] 1660->1662 1663 race_White <= 0.5 entropy = 0.887 samples = 23 value = [16, 7] 1662->1663 1684 entropy = 0.0 samples = 3 value = [3, 0] 1662->1684 1664 entropy = 0.0 samples = 1 value = [1, 0] 1663->1664 1665 education <= 10.5 entropy = 0.902 samples = 22 value = [15, 7] 1663->1665 1666 education <= 9.5 entropy = 0.918 samples = 21 value = [14, 7] 1665->1666 1683 entropy = 0.0 samples = 1 value = [1, 0] 1665->1683 1667 hours-per-week <= 48.5 entropy = 0.811 samples = 12 value = [9, 3] 1666->1667 1676 hours-per-week <= 49.0 entropy = 0.991 samples = 9 value = [5, 4] 1666->1676 1668 entropy = 1.0 samples = 2 value = [1, 1] 1667->1668 1669 hours-per-week <= 49.5 entropy = 0.722 samples = 10 value = [8, 2] 1667->1669 1670 entropy = 0.0 samples = 1 value = [1, 0] 1669->1670 1671 age <= 30.5 entropy = 0.764 samples = 9 value = [7, 2] 1669->1671 1672 entropy = 0.918 samples = 3 value = [2, 1] 1671->1672 1673 age <= 31.5 entropy = 0.65 samples = 6 value = [5, 1] 1671->1673 1674 entropy = 0.0 samples = 3 value = [3, 0] 1673->1674 1675 entropy = 0.918 samples = 3 value = [2, 1] 1673->1675 1677 entropy = 0.0 samples = 1 value = [1, 0] 1676->1677 1678 age <= 30.5 entropy = 1.0 samples = 8 value = [4, 4] 1676->1678 1679 entropy = 1.0 samples = 2 value = [1, 1] 1678->1679 1680 age <= 31.5 entropy = 1.0 samples = 6 value = [3, 3] 1678->1680 1681 entropy = 1.0 samples = 4 value = [2, 2] 1680->1681 1682 entropy = 1.0 samples = 2 value = [1, 1] 1680->1682 1686 race_Hispanic <= 0.5 entropy = 0.993 samples = 31 value = [14, 17] 1685->1686 1721 entropy = 0.0 samples = 1 value = [1, 0] 1685->1721 1687 workclass_Private <= 0.5 entropy = 0.987 samples = 30 value = [13, 17] 1686->1687 1720 entropy = 0.0 samples = 1 value = [1, 0] 1686->1720 1688 education <= 9.5 entropy = 0.65 samples = 6 value = [1, 5] 1687->1688 1693 hours-per-week <= 76.0 entropy = 1.0 samples = 24 value = [12, 12] 1687->1693 1689 hours-per-week <= 62.5 entropy = 0.918 samples = 3 value = [1, 2] 1688->1689 1692 entropy = 0.0 samples = 3 value = [0, 3] 1688->1692 1690 entropy = 0.0 samples = 1 value = [1, 0] 1689->1690 1691 entropy = 0.0 samples = 2 value = [0, 2] 1689->1691 1694 hours-per-week <= 68.5 entropy = 0.999 samples = 23 value = [12, 11] 1693->1694 1719 entropy = 0.0 samples = 1 value = [0, 1] 1693->1719 1695 age <= 31.5 entropy = 1.0 samples = 22 value = [11, 11] 1694->1695 1718 entropy = 0.0 samples = 1 value = [1, 0] 1694->1718 1696 hours-per-week <= 55.5 entropy = 0.985 samples = 14 value = [8, 6] 1695->1696 1709 education <= 10.5 entropy = 0.954 samples = 8 value = [3, 5] 1695->1709 1697 age <= 30.5 entropy = 0.722 samples = 5 value = [4, 1] 1696->1697 1702 age <= 30.5 entropy = 0.991 samples = 9 value = [4, 5] 1696->1702 1698 entropy = 0.0 samples = 2 value = [2, 0] 1697->1698 1699 education <= 9.5 entropy = 0.918 samples = 3 value = [2, 1] 1697->1699 1700 entropy = 0.0 samples = 1 value = [1, 0] 1699->1700 1701 entropy = 1.0 samples = 2 value = [1, 1] 1699->1701 1703 education <= 9.5 entropy = 0.722 samples = 5 value = [1, 4] 1702->1703 1706 education <= 9.5 entropy = 0.811 samples = 4 value = [3, 1] 1702->1706 1704 entropy = 0.918 samples = 3 value = [1, 2] 1703->1704 1705 entropy = 0.0 samples = 2 value = [0, 2] 1703->1705 1707 entropy = 0.0 samples = 2 value = [2, 0] 1706->1707 1708 entropy = 1.0 samples = 2 value = [1, 1] 1706->1708 1710 education <= 9.5 entropy = 0.985 samples = 7 value = [3, 4] 1709->1710 1717 entropy = 0.0 samples = 1 value = [0, 1] 1709->1717 1711 hours-per-week <= 57.5 entropy = 0.811 samples = 4 value = [1, 3] 1710->1711 1714 hours-per-week <= 57.5 entropy = 0.918 samples = 3 value = [2, 1] 1710->1714 1712 entropy = 0.0 samples = 2 value = [0, 2] 1711->1712 1713 entropy = 1.0 samples = 2 value = [1, 1] 1711->1713 1715 entropy = 0.0 samples = 2 value = [2, 0] 1714->1715 1716 entropy = 0.0 samples = 1 value = [0, 1] 1714->1716 1723 entropy = 0.0 samples = 3 value = [0, 3] 1722->1723 1724 workclass_Private <= 0.5 entropy = 0.999 samples = 74 value = [38, 36] 1722->1724 1725 race_Asian <= 0.5 entropy = 0.958 samples = 29 value = [18, 11] 1724->1725 1758 hours-per-week <= 65.0 entropy = 0.991 samples = 45 value = [20, 25] 1724->1758 1726 age <= 33.5 entropy = 0.94 samples = 28 value = [18, 10] 1725->1726 1757 entropy = 0.0 samples = 1 value = [0, 1] 1725->1757 1727 entropy = 0.0 samples = 4 value = [4, 0] 1726->1727 1728 hours-per-week <= 77.5 entropy = 0.98 samples = 24 value = [14, 10] 1726->1728 1729 race_Black <= 0.5 entropy = 0.994 samples = 22 value = [12, 10] 1728->1729 1756 entropy = 0.0 samples = 2 value = [2, 0] 1728->1756 1730 education <= 10.5 entropy = 0.985 samples = 21 value = [12, 9] 1729->1730 1755 entropy = 0.0 samples = 1 value = [0, 1] 1729->1755 1731 hours-per-week <= 73.5 entropy = 0.993 samples = 20 value = [11, 9] 1730->1731 1754 entropy = 0.0 samples = 1 value = [1, 0] 1730->1754 1732 education <= 9.5 entropy = 0.982 samples = 19 value = [11, 8] 1731->1732 1753 entropy = 0.0 samples = 1 value = [0, 1] 1731->1753 1733 workclass_Public <= 0.5 entropy = 0.881 samples = 10 value = [7, 3] 1732->1733 1744 race_White <= 0.5 entropy = 0.991 samples = 9 value = [4, 5] 1732->1744 1734 hours-per-week <= 54.5 entropy = 0.918 samples = 9 value = [6, 3] 1733->1734 1743 entropy = 0.0 samples = 1 value = [1, 0] 1733->1743 1735 hours-per-week <= 52.0 entropy = 1.0 samples = 4 value = [2, 2] 1734->1735 1740 age <= 34.5 entropy = 0.722 samples = 5 value = [4, 1] 1734->1740 1736 age <= 34.5 entropy = 0.918 samples = 3 value = [2, 1] 1735->1736 1739 entropy = 0.0 samples = 1 value = [0, 1] 1735->1739 1737 entropy = 0.0 samples = 1 value = [1, 0] 1736->1737 1738 entropy = 1.0 samples = 2 value = [1, 1] 1736->1738 1741 entropy = 0.811 samples = 4 value = [3, 1] 1740->1741 1742 entropy = 0.0 samples = 1 value = [1, 0] 1740->1742 1745 entropy = 0.0 samples = 1 value = [1, 0] 1744->1745 1746 hours-per-week <= 66.0 entropy = 0.954 samples = 8 value = [3, 5] 1744->1746 1747 age <= 34.5 entropy = 0.863 samples = 7 value = [2, 5] 1746->1747 1752 entropy = 0.0 samples = 1 value = [1, 0] 1746->1752 1748 entropy = 0.0 samples = 2 value = [0, 2] 1747->1748 1749 hours-per-week <= 55.0 entropy = 0.971 samples = 5 value = [2, 3] 1747->1749 1750 entropy = 1.0 samples = 2 value = [1, 1] 1749->1750 1751 entropy = 0.918 samples = 3 value = [1, 2] 1749->1751 1759 hours-per-week <= 57.0 entropy = 0.998 samples = 42 value = [20, 22] 1758->1759 1790 entropy = 0.0 samples = 3 value = [0, 3] 1758->1790 1760 hours-per-week <= 54.5 entropy = 0.971 samples = 30 value = [12, 18] 1759->1760 1781 age <= 33.5 entropy = 0.918 samples = 12 value = [8, 4] 1759->1781 1761 hours-per-week <= 52.0 entropy = 0.996 samples = 26 value = [12, 14] 1760->1761 1780 entropy = 0.0 samples = 4 value = [0, 4] 1760->1780 1762 education <= 10.5 entropy = 0.99 samples = 25 value = [11, 14] 1761->1762 1779 entropy = 0.0 samples = 1 value = [1, 0] 1761->1779 1763 education <= 9.5 entropy = 0.971 samples = 20 value = [8, 12] 1762->1763 1776 age <= 34.5 entropy = 0.971 samples = 5 value = [3, 2] 1762->1776 1764 race_Black <= 0.5 entropy = 1.0 samples = 12 value = [6, 6] 1763->1764 1771 age <= 34.5 entropy = 0.811 samples = 8 value = [2, 6] 1763->1771 1765 age <= 33.5 entropy = 0.994 samples = 11 value = [5, 6] 1764->1765 1770 entropy = 0.0 samples = 1 value = [1, 0] 1764->1770 1766 entropy = 0.811 samples = 4 value = [1, 3] 1765->1766 1767 age <= 34.5 entropy = 0.985 samples = 7 value = [4, 3] 1765->1767 1768 entropy = 0.811 samples = 4 value = [3, 1] 1767->1768 1769 entropy = 0.918 samples = 3 value = [1, 2] 1767->1769 1772 entropy = 0.0 samples = 4 value = [0, 4] 1771->1772 1773 race_White <= 0.5 entropy = 1.0 samples = 4 value = [2, 2] 1771->1773 1774 entropy = 0.0 samples = 1 value = [0, 1] 1773->1774 1775 entropy = 0.918 samples = 3 value = [2, 1] 1773->1775 1777 entropy = 0.0 samples = 2 value = [2, 0] 1776->1777 1778 entropy = 0.918 samples = 3 value = [1, 2] 1776->1778 1782 entropy = 0.0 samples = 4 value = [4, 0] 1781->1782 1783 age <= 34.5 entropy = 1.0 samples = 8 value = [4, 4] 1781->1783 1784 entropy = 0.0 samples = 3 value = [0, 3] 1783->1784 1785 education <= 9.5 entropy = 0.722 samples = 5 value = [4, 1] 1783->1785 1786 entropy = 0.0 samples = 2 value = [2, 0] 1785->1786 1787 hours-per-week <= 59.0 entropy = 0.918 samples = 3 value = [2, 1] 1785->1787 1788 entropy = 0.0 samples = 1 value = [1, 0] 1787->1788 1789 entropy = 1.0 samples = 2 value = [1, 1] 1787->1789 1792 education <= 9.5 entropy = 0.989 samples = 1566 value = [880, 686] 1791->1792 2877 workclass_Public <= 0.5 entropy = 0.791 samples = 101 value = [77, 24] 1791->2877 1793 race_Amer-Indian <= 0.5 entropy = 0.969 samples = 924 value = [557, 367] 1792->1793 2368 hours-per-week <= 43.5 entropy = 1.0 samples = 642 value = [323, 319] 1792->2368 1794 race_Hispanic <= 0.5 entropy = 0.972 samples = 912 value = [546, 366] 1793->1794 2365 age <= 58.0 entropy = 0.414 samples = 12 value = [11, 1] 1793->2365 1795 sex_Male <= 0.5 entropy = 0.973 samples = 908 value = [542, 366] 1794->1795 2364 entropy = 0.0 samples = 4 value = [4, 0] 1794->2364 1796 age <= 53.5 entropy = 0.913 samples = 125 value = [84, 41] 1795->1796 1879 hours-per-week <= 61.5 entropy = 0.979 samples = 783 value = [458, 325] 1795->1879 1797 workclass_Public <= 0.5 entropy = 0.979 samples = 89 value = [52, 37] 1796->1797 1864 age <= 57.5 entropy = 0.503 samples = 36 value = [32, 4] 1796->1864 1798 hours-per-week <= 41.5 entropy = 0.948 samples = 79 value = [50, 29] 1797->1798 1859 age <= 41.225 entropy = 0.722 samples = 10 value = [2, 8] 1797->1859 1799 workclass_Self-emp <= 0.5 entropy = 0.986 samples = 65 value = [37, 28] 1798->1799 1856 race_Black <= 0.5 entropy = 0.371 samples = 14 value = [13, 1] 1798->1856 1800 race_Asian <= 0.5 entropy = 0.965 samples = 59 value = [36, 23] 1799->1800 1851 age <= 42.5 entropy = 0.65 samples = 6 value = [1, 5] 1799->1851 1801 race_Black <= 0.5 entropy = 0.958 samples = 58 value = [36, 22] 1800->1801 1850 entropy = 0.0 samples = 1 value = [0, 1] 1800->1850 1802 age <= 52.5 entropy = 0.982 samples = 45 value = [26, 19] 1801->1802 1843 age <= 45.5 entropy = 0.779 samples = 13 value = [10, 3] 1801->1843 1803 age <= 51.0 entropy = 0.976 samples = 44 value = [26, 18] 1802->1803 1842 entropy = 0.0 samples = 1 value = [0, 1] 1802->1842 1804 age <= 45.5 entropy = 0.985 samples = 42 value = [24, 18] 1803->1804 1841 entropy = 0.0 samples = 2 value = [2, 0] 1803->1841 1805 age <= 44.5 entropy = 0.951 samples = 27 value = [17, 10] 1804->1805 1830 hours-per-week <= 39.0 entropy = 0.997 samples = 15 value = [7, 8] 1804->1830 1806 age <= 38.725 entropy = 0.961 samples = 26 value = [16, 10] 1805->1806 1829 entropy = 0.0 samples = 1 value = [1, 0] 1805->1829 1807 age <= 37.5 entropy = 0.863 samples = 7 value = [5, 2] 1806->1807 1812 age <= 39.5 entropy = 0.982 samples = 19 value = [11, 8] 1806->1812 1808 age <= 36.5 entropy = 1.0 samples = 4 value = [2, 2] 1807->1808 1811 entropy = 0.0 samples = 3 value = [3, 0] 1807->1811 1809 entropy = 0.918 samples = 3 value = [2, 1] 1808->1809 1810 entropy = 0.0 samples = 1 value = [0, 1] 1808->1810 1813 hours-per-week <= 38.0 entropy = 0.971 samples = 5 value = [2, 3] 1812->1813 1816 hours-per-week <= 37.0 entropy = 0.94 samples = 14 value = [9, 5] 1812->1816 1814 entropy = 0.0 samples = 1 value = [1, 0] 1813->1814 1815 entropy = 0.811 samples = 4 value = [1, 3] 1813->1815 1817 age <= 41.0 entropy = 0.918 samples = 3 value = [1, 2] 1816->1817 1820 age <= 40.5 entropy = 0.845 samples = 11 value = [8, 3] 1816->1820 1818 entropy = 0.0 samples = 1 value = [0, 1] 1817->1818 1819 entropy = 1.0 samples = 2 value = [1, 1] 1817->1819 1821 entropy = 0.0 samples = 2 value = [2, 0] 1820->1821 1822 hours-per-week <= 39.0 entropy = 0.918 samples = 9 value = [6, 3] 1820->1822 1823 entropy = 0.0 samples = 1 value = [1, 0] 1822->1823 1824 age <= 41.5 entropy = 0.954 samples = 8 value = [5, 3] 1822->1824 1825 entropy = 1.0 samples = 2 value = [1, 1] 1824->1825 1826 age <= 43.0 entropy = 0.918 samples = 6 value = [4, 2] 1824->1826 1827 entropy = 0.811 samples = 4 value = [3, 1] 1826->1827 1828 entropy = 1.0 samples = 2 value = [1, 1] 1826->1828 1831 entropy = 0.0 samples = 1 value = [0, 1] 1830->1831 1832 age <= 48.5 entropy = 1.0 samples = 14 value = [7, 7] 1830->1832 1833 age <= 47.5 entropy = 0.991 samples = 9 value = [5, 4] 1832->1833 1838 age <= 49.5 entropy = 0.971 samples = 5 value = [2, 3] 1832->1838 1834 age <= 46.5 entropy = 1.0 samples = 8 value = [4, 4] 1833->1834 1837 entropy = 0.0 samples = 1 value = [1, 0] 1833->1837 1835 entropy = 1.0 samples = 6 value = [3, 3] 1834->1835 1836 entropy = 1.0 samples = 2 value = [1, 1] 1834->1836 1839 entropy = 0.0 samples = 1 value = [0, 1] 1838->1839 1840 entropy = 1.0 samples = 4 value = [2, 2] 1838->1840 1844 entropy = 0.0 samples = 5 value = [5, 0] 1843->1844 1845 age <= 48.0 entropy = 0.954 samples = 8 value = [5, 3] 1843->1845 1846 entropy = 0.0 samples = 2 value = [0, 2] 1845->1846 1847 age <= 50.0 entropy = 0.65 samples = 6 value = [5, 1] 1845->1847 1848 entropy = 0.918 samples = 3 value = [2, 1] 1847->1848 1849 entropy = 0.0 samples = 3 value = [3, 0] 1847->1849 1852 age <= 39.0 entropy = 0.918 samples = 3 value = [1, 2] 1851->1852 1855 entropy = 0.0 samples = 3 value = [0, 3] 1851->1855 1853 entropy = 0.0 samples = 2 value = [0, 2] 1852->1853 1854 entropy = 0.0 samples = 1 value = [1, 0] 1852->1854 1857 entropy = 0.0 samples = 12 value = [12, 0] 1856->1857 1858 entropy = 1.0 samples = 2 value = [1, 1] 1856->1858 1860 age <= 37.0 entropy = 0.918 samples = 3 value = [2, 1] 1859->1860 1863 entropy = 0.0 samples = 7 value = [0, 7] 1859->1863 1861 entropy = 0.0 samples = 1 value = [1, 0] 1860->1861 1862 entropy = 1.0 samples = 2 value = [1, 1] 1860->1862 1865 entropy = 0.0 samples = 12 value = [12, 0] 1864->1865 1866 hours-per-week <= 39.0 entropy = 0.65 samples = 24 value = [20, 4] 1864->1866 1867 age <= 60.0 entropy = 1.0 samples = 6 value = [3, 3] 1866->1867 1872 age <= 60.5 entropy = 0.31 samples = 18 value = [17, 1] 1866->1872 1868 hours-per-week <= 36.0 entropy = 0.811 samples = 4 value = [1, 3] 1867->1868 1871 entropy = 0.0 samples = 2 value = [2, 0] 1867->1871 1869 entropy = 0.0 samples = 2 value = [0, 2] 1868->1869 1870 entropy = 1.0 samples = 2 value = [1, 1] 1868->1870 1873 age <= 59.5 entropy = 0.469 samples = 10 value = [9, 1] 1872->1873 1878 entropy = 0.0 samples = 8 value = [8, 0] 1872->1878 1874 entropy = 0.0 samples = 6 value = [6, 0] 1873->1874 1875 race_Black <= 0.5 entropy = 0.811 samples = 4 value = [3, 1] 1873->1875 1876 entropy = 0.918 samples = 3 value = [2, 1] 1875->1876 1877 entropy = 0.0 samples = 1 value = [1, 0] 1875->1877 1880 hours-per-week <= 41.0 entropy = 0.983 samples = 743 value = [429, 314] 1879->1880 2335 age <= 52.5 entropy = 0.849 samples = 40 value = [29, 11] 1879->2335 1881 age <= 37.5 entropy = 0.963 samples = 462 value = [283, 179] 1880->1881 2094 hours-per-week <= 42.5 entropy = 0.999 samples = 281 value = [146, 135] 1880->2094 1882 race_White <= 0.5 entropy = 0.859 samples = 46 value = [33, 13] 1881->1882 1895 age <= 59.5 entropy = 0.97 samples = 416 value = [250, 166] 1881->1895 1883 entropy = 0.0 samples = 4 value = [4, 0] 1882->1883 1884 hours-per-week <= 37.5 entropy = 0.893 samples = 42 value = [29, 13] 1882->1884 1885 entropy = 0.0 samples = 1 value = [0, 1] 1884->1885 1886 workclass_Public <= 0.5 entropy = 0.872 samples = 41 value = [29, 12] 1884->1886 1887 workclass_Private <= 0.5 entropy = 0.834 samples = 34 value = [25, 9] 1886->1887 1892 age <= 36.5 entropy = 0.985 samples = 7 value = [4, 3] 1886->1892 1888 entropy = 0.0 samples = 1 value = [1, 0] 1887->1888 1889 age <= 36.5 entropy = 0.845 samples = 33 value = [24, 9] 1887->1889 1890 entropy = 0.837 samples = 15 value = [11, 4] 1889->1890 1891 entropy = 0.852 samples = 18 value = [13, 5] 1889->1891 1893 entropy = 1.0 samples = 4 value = [2, 2] 1892->1893 1894 entropy = 0.918 samples = 3 value = [2, 1] 1892->1894 1896 race_Asian <= 0.5 entropy = 0.976 samples = 387 value = [229, 158] 1895->1896 2079 race_White <= 0.5 entropy = 0.85 samples = 29 value = [21, 8] 1895->2079 1897 age <= 52.5 entropy = 0.973 samples = 382 value = [228, 154] 1896->1897 2076 workclass_Private <= 0.5 entropy = 0.722 samples = 5 value = [1, 4] 1896->2076 1898 age <= 38.225 entropy = 0.958 samples = 295 value = [183, 112] 1897->1898 2033 race_White <= 0.5 entropy = 0.999 samples = 87 value = [45, 42] 1897->2033 1899 workclass_Self-emp <= 0.5 entropy = 0.993 samples = 20 value = [9, 11] 1898->1899 1908 workclass_Private <= 0.5 entropy = 0.949 samples = 275 value = [174, 101] 1898->1908 1900 race_Black <= 0.5 entropy = 1.0 samples = 18 value = [9, 9] 1899->1900 1907 entropy = 0.0 samples = 2 value = [0, 2] 1899->1907 1901 hours-per-week <= 37.5 entropy = 0.998 samples = 17 value = [9, 8] 1900->1901 1906 entropy = 0.0 samples = 1 value = [0, 1] 1900->1906 1902 entropy = 0.0 samples = 1 value = [1, 0] 1901->1902 1903 workclass_Public <= 0.5 entropy = 1.0 samples = 16 value = [8, 8] 1901->1903 1904 entropy = 1.0 samples = 14 value = [7, 7] 1903->1904 1905 entropy = 1.0 samples = 2 value = [1, 1] 1903->1905 1909 age <= 46.5 entropy = 0.978 samples = 87 value = [51, 36] 1908->1909 1976 age <= 40.5 entropy = 0.93 samples = 188 value = [123, 65] 1908->1976 1910 race_Black <= 0.5 entropy = 0.907 samples = 59 value = [40, 19] 1909->1910 1953 age <= 51.5 entropy = 0.967 samples = 28 value = [11, 17] 1909->1953 1911 age <= 45.5 entropy = 0.918 samples = 57 value = [38, 19] 1910->1911 1952 entropy = 0.0 samples = 2 value = [2, 0] 1910->1952 1912 workclass_Public <= 0.5 entropy = 0.943 samples = 50 value = [32, 18] 1911->1912 1949 workclass_Self-emp <= 0.5 entropy = 0.592 samples = 7 value = [6, 1] 1911->1949 1913 age <= 38.725 entropy = 0.89 samples = 26 value = [18, 8] 1912->1913 1932 hours-per-week <= 39.0 entropy = 0.98 samples = 24 value = [14, 10] 1912->1932 1914 hours-per-week <= 37.5 entropy = 0.592 samples = 7 value = [6, 1] 1913->1914 1917 age <= 39.5 entropy = 0.949 samples = 19 value = [12, 7] 1913->1917 1915 entropy = 0.0 samples = 2 value = [2, 0] 1914->1915 1916 entropy = 0.722 samples = 5 value = [4, 1] 1914->1916 1918 entropy = 1.0 samples = 4 value = [2, 2] 1917->1918 1919 age <= 40.5 entropy = 0.918 samples = 15 value = [10, 5] 1917->1919 1920 entropy = 0.0 samples = 2 value = [2, 0] 1919->1920 1921 age <= 42.0 entropy = 0.961 samples = 13 value = [8, 5] 1919->1921 1922 hours-per-week <= 39.0 entropy = 1.0 samples = 4 value = [2, 2] 1921->1922 1925 age <= 43.5 entropy = 0.918 samples = 9 value = [6, 3] 1921->1925 1923 entropy = 0.0 samples = 1 value = [1, 0] 1922->1923 1924 entropy = 0.918 samples = 3 value = [1, 2] 1922->1924 1926 hours-per-week <= 37.5 entropy = 0.722 samples = 5 value = [4, 1] 1925->1926 1929 age <= 44.5 entropy = 1.0 samples = 4 value = [2, 2] 1925->1929 1927 entropy = 1.0 samples = 2 value = [1, 1] 1926->1927 1928 entropy = 0.0 samples = 3 value = [3, 0] 1926->1928 1930 entropy = 0.0 samples = 1 value = [0, 1] 1929->1930 1931 entropy = 0.918 samples = 3 value = [2, 1] 1929->1931 1933 entropy = 0.0 samples = 1 value = [0, 1] 1932->1933 1934 age <= 39.5 entropy = 0.966 samples = 23 value = [14, 9] 1932->1934 1935 age <= 38.725 entropy = 0.985 samples = 7 value = [3, 4] 1934->1935 1938 age <= 40.5 entropy = 0.896 samples = 16 value = [11, 5] 1934->1938 1936 entropy = 0.971 samples = 5 value = [2, 3] 1935->1936 1937 entropy = 1.0 samples = 2 value = [1, 1] 1935->1937 1939 entropy = 0.0 samples = 2 value = [2, 0] 1938->1939 1940 age <= 42.5 entropy = 0.94 samples = 14 value = [9, 5] 1938->1940 1941 age <= 41.5 entropy = 1.0 samples = 6 value = [3, 3] 1940->1941 1944 age <= 44.5 entropy = 0.811 samples = 8 value = [6, 2] 1940->1944 1942 entropy = 0.971 samples = 5 value = [3, 2] 1941->1942 1943 entropy = 0.0 samples = 1 value = [0, 1] 1941->1943 1945 age <= 43.5 entropy = 0.65 samples = 6 value = [5, 1] 1944->1945 1948 entropy = 1.0 samples = 2 value = [1, 1] 1944->1948 1946 entropy = 0.722 samples = 5 value = [4, 1] 1945->1946 1947 entropy = 0.0 samples = 1 value = [1, 0] 1945->1947 1950 entropy = 0.65 samples = 6 value = [5, 1] 1949->1950 1951 entropy = 0.0 samples = 1 value = [1, 0] 1949->1951 1954 workclass_Self-emp <= 0.5 entropy = 0.904 samples = 25 value = [8, 17] 1953->1954 1975 entropy = 0.0 samples = 3 value = [3, 0] 1953->1975 1955 age <= 47.5 entropy = 0.949 samples = 19 value = [7, 12] 1954->1955 1970 age <= 48.5 entropy = 0.65 samples = 6 value = [1, 5] 1954->1970 1956 race_White <= 0.5 entropy = 0.918 samples = 6 value = [2, 4] 1955->1956 1959 race_White <= 0.5 entropy = 0.961 samples = 13 value = [5, 8] 1955->1959 1957 entropy = 0.0 samples = 2 value = [0, 2] 1956->1957 1958 entropy = 1.0 samples = 4 value = [2, 2] 1956->1958 1960 age <= 49.5 entropy = 0.918 samples = 3 value = [2, 1] 1959->1960 1963 age <= 48.5 entropy = 0.881 samples = 10 value = [3, 7] 1959->1963 1961 entropy = 0.0 samples = 1 value = [1, 0] 1960->1961 1962 entropy = 1.0 samples = 2 value = [1, 1] 1960->1962 1964 entropy = 1.0 samples = 2 value = [1, 1] 1963->1964 1965 age <= 50.5 entropy = 0.811 samples = 8 value = [2, 6] 1963->1965 1966 age <= 49.5 entropy = 0.722 samples = 5 value = [1, 4] 1965->1966 1969 entropy = 0.918 samples = 3 value = [1, 2] 1965->1969 1967 entropy = 0.811 samples = 4 value = [1, 3] 1966->1967 1968 entropy = 0.0 samples = 1 value = [0, 1] 1966->1968 1971 entropy = 0.0 samples = 3 value = [0, 3] 1970->1971 1972 age <= 49.5 entropy = 0.918 samples = 3 value = [1, 2] 1970->1972 1973 entropy = 1.0 samples = 2 value = [1, 1] 1972->1973 1974 entropy = 0.0 samples = 1 value = [0, 1] 1972->1974 1977 race_White <= 0.5 entropy = 0.977 samples = 39 value = [23, 16] 1976->1977 1988 age <= 49.5 entropy = 0.914 samples = 149 value = [100, 49] 1976->1988 1978 age <= 39.5 entropy = 0.722 samples = 5 value = [4, 1] 1977->1978 1981 hours-per-week <= 39.0 entropy = 0.99 samples = 34 value = [19, 15] 1977->1981 1979 entropy = 0.0 samples = 3 value = [3, 0] 1978->1979 1980 entropy = 1.0 samples = 2 value = [1, 1] 1978->1980 1982 entropy = 0.0 samples = 1 value = [1, 0] 1981->1982 1983 age <= 38.725 entropy = 0.994 samples = 33 value = [18, 15] 1981->1983 1984 entropy = 0.971 samples = 5 value = [2, 3] 1983->1984 1985 age <= 39.5 entropy = 0.985 samples = 28 value = [16, 12] 1983->1985 1986 entropy = 0.991 samples = 18 value = [10, 8] 1985->1986 1987 entropy = 0.971 samples = 10 value = [6, 4] 1985->1987 1989 age <= 46.5 entropy = 0.89 samples = 114 value = [79, 35] 1988->1989 2022 age <= 50.5 entropy = 0.971 samples = 35 value = [21, 14] 1988->2022 1990 race_White <= 0.5 entropy = 0.922 samples = 80 value = [53, 27] 1989->1990 2013 race_White <= 0.5 entropy = 0.787 samples = 34 value = [26, 8] 1989->2013 1991 age <= 42.5 entropy = 1.0 samples = 10 value = [5, 5] 1990->1991 1998 age <= 42.5 entropy = 0.898 samples = 70 value = [48, 22] 1990->1998 1992 entropy = 0.0 samples = 3 value = [0, 3] 1991->1992 1993 age <= 45.5 entropy = 0.863 samples = 7 value = [5, 2] 1991->1993 1994 age <= 43.5 entropy = 0.65 samples = 6 value = [5, 1] 1993->1994 1997 entropy = 0.0 samples = 1 value = [0, 1] 1993->1997 1995 entropy = 1.0 samples = 2 value = [1, 1] 1994->1995 1996 entropy = 0.0 samples = 4 value = [4, 0] 1994->1996 1999 hours-per-week <= 37.5 entropy = 0.75 samples = 28 value = [22, 6] 1998->1999 2004 hours-per-week <= 38.0 entropy = 0.959 samples = 42 value = [26, 16] 1998->2004 2000 entropy = 1.0 samples = 2 value = [1, 1] 1999->2000 2001 age <= 41.5 entropy = 0.706 samples = 26 value = [21, 5] 1999->2001 2002 entropy = 0.787 samples = 17 value = [13, 4] 2001->2002 2003 entropy = 0.503 samples = 9 value = [8, 1] 2001->2003 2005 entropy = 0.0 samples = 2 value = [2, 0] 2004->2005 2006 age <= 44.5 entropy = 0.971 samples = 40 value = [24, 16] 2004->2006 2007 age <= 43.5 entropy = 0.966 samples = 23 value = [14, 9] 2006->2007 2010 age <= 45.5 entropy = 0.977 samples = 17 value = [10, 7] 2006->2010 2008 entropy = 0.971 samples = 10 value = [6, 4] 2007->2008 2009 entropy = 0.961 samples = 13 value = [8, 5] 2007->2009 2011 entropy = 0.985 samples = 7 value = [4, 3] 2010->2011 2012 entropy = 0.971 samples = 10 value = [6, 4] 2010->2012 2014 entropy = 0.0 samples = 3 value = [3, 0] 2013->2014 2015 hours-per-week <= 39.0 entropy = 0.824 samples = 31 value = [23, 8] 2013->2015 2016 entropy = 0.0 samples = 2 value = [2, 0] 2015->2016 2017 age <= 47.5 entropy = 0.85 samples = 29 value = [21, 8] 2015->2017 2018 entropy = 0.845 samples = 11 value = [8, 3] 2017->2018 2019 age <= 48.5 entropy = 0.852 samples = 18 value = [13, 5] 2017->2019 2020 entropy = 0.863 samples = 7 value = [5, 2] 2019->2020 2021 entropy = 0.845 samples = 11 value = [8, 3] 2019->2021 2023 race_White <= 0.5 entropy = 0.971 samples = 10 value = [4, 6] 2022->2023 2028 age <= 51.5 entropy = 0.904 samples = 25 value = [17, 8] 2022->2028 2024 entropy = 0.0 samples = 1 value = [1, 0] 2023->2024 2025 hours-per-week <= 39.0 entropy = 0.918 samples = 9 value = [3, 6] 2023->2025 2026 entropy = 0.0 samples = 1 value = [0, 1] 2025->2026 2027 entropy = 0.954 samples = 8 value = [3, 5] 2025->2027 2029 race_White <= 0.5 entropy = 0.896 samples = 16 value = [11, 5] 2028->2029 2032 entropy = 0.918 samples = 9 value = [6, 3] 2028->2032 2030 entropy = 0.918 samples = 3 value = [2, 1] 2029->2030 2031 entropy = 0.89 samples = 13 value = [9, 4] 2029->2031 2034 age <= 55.5 entropy = 0.811 samples = 8 value = [6, 2] 2033->2034 2039 hours-per-week <= 36.5 entropy = 1.0 samples = 79 value = [39, 40] 2033->2039 2035 entropy = 0.0 samples = 5 value = [5, 0] 2034->2035 2036 age <= 58.5 entropy = 0.918 samples = 3 value = [1, 2] 2034->2036 2037 entropy = 0.0 samples = 2 value = [0, 2] 2036->2037 2038 entropy = 0.0 samples = 1 value = [1, 0] 2036->2038 2040 workclass_Public <= 0.5 entropy = 0.811 samples = 4 value = [3, 1] 2039->2040 2043 age <= 56.5 entropy = 0.999 samples = 75 value = [36, 39] 2039->2043 2041 entropy = 0.0 samples = 3 value = [3, 0] 2040->2041 2042 entropy = 0.0 samples = 1 value = [0, 1] 2040->2042 2044 age <= 53.5 entropy = 0.989 samples = 41 value = [18, 23] 2043->2044 2061 age <= 58.5 entropy = 0.998 samples = 34 value = [18, 16] 2043->2061 2045 workclass_Public <= 0.5 entropy = 0.997 samples = 15 value = [8, 7] 2044->2045 2052 workclass_Public <= 0.5 entropy = 0.961 samples = 26 value = [10, 16] 2044->2052 2046 hours-per-week <= 39.0 entropy = 0.996 samples = 13 value = [6, 7] 2045->2046 2051 entropy = 0.0 samples = 2 value = [2, 0] 2045->2051 2047 entropy = 0.0 samples = 1 value = [1, 0] 2046->2047 2048 workclass_Private <= 0.5 entropy = 0.98 samples = 12 value = [5, 7] 2046->2048 2049 entropy = 0.0 samples = 1 value = [0, 1] 2048->2049 2050 entropy = 0.994 samples = 11 value = [5, 6] 2048->2050 2053 workclass_Private <= 0.5 entropy = 0.994 samples = 22 value = [10, 12] 2052->2053 2060 entropy = 0.0 samples = 4 value = [0, 4] 2052->2060 2054 entropy = 0.0 samples = 1 value = [1, 0] 2053->2054 2055 age <= 55.5 entropy = 0.985 samples = 21 value = [9, 12] 2053->2055 2056 age <= 54.5 entropy = 0.971 samples = 15 value = [6, 9] 2055->2056 2059 entropy = 1.0 samples = 6 value = [3, 3] 2055->2059 2057 entropy = 0.985 samples = 7 value = [3, 4] 2056->2057 2058 entropy = 0.954 samples = 8 value = [3, 5] 2056->2058 2062 age <= 57.5 entropy = 0.959 samples = 21 value = [13, 8] 2061->2062 2071 workclass_Self-emp <= 0.5 entropy = 0.961 samples = 13 value = [5, 8] 2061->2071 2063 workclass_Self-emp <= 0.5 entropy = 0.985 samples = 7 value = [4, 3] 2062->2063 2066 workclass_Self-emp <= 0.5 entropy = 0.94 samples = 14 value = [9, 5] 2062->2066 2064 entropy = 1.0 samples = 6 value = [3, 3] 2063->2064 2065 entropy = 0.0 samples = 1 value = [1, 0] 2063->2065 2067 workclass_Public <= 0.5 entropy = 0.918 samples = 12 value = [8, 4] 2066->2067 2070 entropy = 1.0 samples = 2 value = [1, 1] 2066->2070 2068 entropy = 0.918 samples = 9 value = [6, 3] 2067->2068 2069 entropy = 0.918 samples = 3 value = [2, 1] 2067->2069 2072 workclass_Public <= 0.5 entropy = 0.994 samples = 11 value = [5, 6] 2071->2072 2075 entropy = 0.0 samples = 2 value = [0, 2] 2071->2075 2073 entropy = 0.954 samples = 8 value = [3, 5] 2072->2073 2074 entropy = 0.918 samples = 3 value = [2, 1] 2072->2074 2077 entropy = 1.0 samples = 2 value = [1, 1] 2076->2077 2078 entropy = 0.0 samples = 3 value = [0, 3] 2076->2078 2080 entropy = 0.0 samples = 2 value = [2, 0] 2079->2080 2081 workclass_Public <= 0.5 entropy = 0.877 samples = 27 value = [19, 8] 2079->2081 2082 age <= 61.5 entropy = 0.904 samples = 25 value = [17, 8] 2081->2082 2093 entropy = 0.0 samples = 2 value = [2, 0] 2081->2093 2083 hours-per-week <= 37.5 entropy = 0.863 samples = 21 value = [15, 6] 2082->2083 2090 workclass_Private <= 0.5 entropy = 1.0 samples = 4 value = [2, 2] 2082->2090 2084 entropy = 1.0 samples = 2 value = [1, 1] 2083->2084 2085 workclass_Private <= 0.5 entropy = 0.831 samples = 19 value = [14, 5] 2083->2085 2086 entropy = 0.0 samples = 2 value = [2, 0] 2085->2086 2087 age <= 60.5 entropy = 0.874 samples = 17 value = [12, 5] 2085->2087 2088 entropy = 0.946 samples = 11 value = [7, 4] 2087->2088 2089 entropy = 0.65 samples = 6 value = [5, 1] 2087->2089 2091 entropy = 0.0 samples = 1 value = [1, 0] 2090->2091 2092 entropy = 0.918 samples = 3 value = [1, 2] 2090->2092 2095 entropy = 0.0 samples = 5 value = [0, 5] 2094->2095 2096 age <= 61.5 entropy = 0.998 samples = 276 value = [146, 130] 2094->2096 2097 age <= 43.5 entropy = 0.997 samples = 274 value = [146, 128] 2096->2097 2334 entropy = 0.0 samples = 2 value = [0, 2] 2096->2334 2098 age <= 40.5 entropy = 0.979 samples = 111 value = [65, 46] 2097->2098 2181 age <= 54.5 entropy = 1.0 samples = 163 value = [81, 82] 2097->2181 2099 hours-per-week <= 54.5 entropy = 0.998 samples = 84 value = [44, 40] 2098->2099 2160 hours-per-week <= 43.5 entropy = 0.764 samples = 27 value = [21, 6] 2098->2160 2100 hours-per-week <= 51.5 entropy = 0.999 samples = 60 value = [29, 31] 2099->2100 2143 hours-per-week <= 58.0 entropy = 0.954 samples = 24 value = [15, 9] 2099->2143 2101 age <= 38.225 entropy = 1.0 samples = 58 value = [29, 29] 2100->2101 2142 entropy = 0.0 samples = 2 value = [0, 2] 2100->2142 2102 age <= 37.5 entropy = 0.982 samples = 38 value = [22, 16] 2101->2102 2129 age <= 39.5 entropy = 0.934 samples = 20 value = [7, 13] 2101->2129 2103 workclass_Self-emp <= 0.5 entropy = 0.999 samples = 23 value = [11, 12] 2102->2103 2118 hours-per-week <= 46.5 entropy = 0.837 samples = 15 value = [11, 4] 2102->2118 2104 hours-per-week <= 49.0 entropy = 0.971 samples = 20 value = [8, 12] 2103->2104 2117 entropy = 0.0 samples = 3 value = [3, 0] 2103->2117 2105 hours-per-week <= 44.5 entropy = 0.996 samples = 13 value = [7, 6] 2104->2105 2114 hours-per-week <= 50.5 entropy = 0.592 samples = 7 value = [1, 6] 2104->2114 2106 entropy = 0.0 samples = 2 value = [0, 2] 2105->2106 2107 age <= 36.5 entropy = 0.946 samples = 11 value = [7, 4] 2105->2107 2108 hours-per-week <= 46.5 entropy = 0.65 samples = 6 value = [5, 1] 2107->2108 2111 hours-per-week <= 45.5 entropy = 0.971 samples = 5 value = [2, 3] 2107->2111 2109 entropy = 0.722 samples = 5 value = [4, 1] 2108->2109 2110 entropy = 0.0 samples = 1 value = [1, 0] 2108->2110 2112 entropy = 1.0 samples = 4 value = [2, 2] 2111->2112 2113 entropy = 0.0 samples = 1 value = [0, 1] 2111->2113 2115 entropy = 0.0 samples = 6 value = [0, 6] 2114->2115 2116 entropy = 0.0 samples = 1 value = [1, 0] 2114->2116 2119 entropy = 0.0 samples = 6 value = [6, 0] 2118->2119 2120 workclass_Public <= 0.5 entropy = 0.991 samples = 9 value = [5, 4] 2118->2120 2121 race_Asian <= 0.5 entropy = 0.954 samples = 8 value = [5, 3] 2120->2121 2128 entropy = 0.0 samples = 1 value = [0, 1] 2120->2128 2122 hours-per-week <= 49.0 entropy = 0.985 samples = 7 value = [4, 3] 2121->2122 2127 entropy = 0.0 samples = 1 value = [1, 0] 2121->2127 2123 entropy = 0.0 samples = 1 value = [0, 1] 2122->2123 2124 workclass_Private <= 0.5 entropy = 0.918 samples = 6 value = [4, 2] 2122->2124 2125 entropy = 1.0 samples = 2 value = [1, 1] 2124->2125 2126 entropy = 0.811 samples = 4 value = [3, 1] 2124->2126 2130 hours-per-week <= 49.5 entropy = 0.619 samples = 13 value = [2, 11] 2129->2130 2137 hours-per-week <= 47.5 entropy = 0.863 samples = 7 value = [5, 2] 2129->2137 2131 entropy = 0.0 samples = 6 value = [0, 6] 2130->2131 2132 age <= 38.725 entropy = 0.863 samples = 7 value = [2, 5] 2130->2132 2133 entropy = 0.0 samples = 1 value = [1, 0] 2132->2133 2134 workclass_Self-emp <= 0.5 entropy = 0.65 samples = 6 value = [1, 5] 2132->2134 2135 entropy = 0.0 samples = 4 value = [0, 4] 2134->2135 2136 entropy = 1.0 samples = 2 value = [1, 1] 2134->2136 2138 entropy = 1.0 samples = 2 value = [1, 1] 2137->2138 2139 workclass_Private <= 0.5 entropy = 0.722 samples = 5 value = [4, 1] 2137->2139 2140 entropy = 0.918 samples = 3 value = [2, 1] 2139->2140 2141 entropy = 0.0 samples = 2 value = [2, 0] 2139->2141 2144 entropy = 0.0 samples = 4 value = [4, 0] 2143->2144 2145 age <= 37.5 entropy = 0.993 samples = 20 value = [11, 9] 2143->2145 2146 age <= 36.5 entropy = 0.971 samples = 10 value = [4, 6] 2145->2146 2153 workclass_Private <= 0.5 entropy = 0.881 samples = 10 value = [7, 3] 2145->2153 2147 workclass_Private <= 0.5 entropy = 1.0 samples = 6 value = [3, 3] 2146->2147 2150 workclass_Self-emp <= 0.5 entropy = 0.811 samples = 4 value = [1, 3] 2146->2150 2148 entropy = 0.0 samples = 1 value = [0, 1] 2147->2148 2149 entropy = 0.971 samples = 5 value = [3, 2] 2147->2149 2151 entropy = 0.0 samples = 1 value = [0, 1] 2150->2151 2152 entropy = 0.918 samples = 3 value = [1, 2] 2150->2152 2154 entropy = 0.0 samples = 5 value = [5, 0] 2153->2154 2155 age <= 39.5 entropy = 0.971 samples = 5 value = [2, 3] 2153->2155 2156 race_Black <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 2155->2156 2159 entropy = 0.0 samples = 2 value = [0, 2] 2155->2159 2157 entropy = 0.0 samples = 2 value = [2, 0] 2156->2157 2158 entropy = 0.0 samples = 1 value = [0, 1] 2156->2158 2161 entropy = 0.0 samples = 1 value = [0, 1] 2160->2161 2162 workclass_Private <= 0.5 entropy = 0.706 samples = 26 value = [21, 5] 2160->2162 2163 entropy = 0.0 samples = 7 value = [7, 0] 2162->2163 2164 hours-per-week <= 44.5 entropy = 0.831 samples = 19 value = [14, 5] 2162->2164 2165 entropy = 0.0 samples = 2 value = [2, 0] 2164->2165 2166 hours-per-week <= 55.0 entropy = 0.874 samples = 17 value = [12, 5] 2164->2166 2167 hours-per-week <= 48.5 entropy = 0.918 samples = 15 value = [10, 5] 2166->2167 2180 entropy = 0.0 samples = 2 value = [2, 0] 2166->2180 2168 age <= 41.5 entropy = 0.764 samples = 9 value = [7, 2] 2167->2168 2173 age <= 42.5 entropy = 1.0 samples = 6 value = [3, 3] 2167->2173 2169 entropy = 0.0 samples = 3 value = [3, 0] 2168->2169 2170 hours-per-week <= 46.5 entropy = 0.918 samples = 6 value = [4, 2] 2168->2170 2171 entropy = 0.918 samples = 3 value = [2, 1] 2170->2171 2172 entropy = 0.918 samples = 3 value = [2, 1] 2170->2172 2174 hours-per-week <= 49.5 entropy = 0.971 samples = 5 value = [2, 3] 2173->2174 2179 entropy = 0.0 samples = 1 value = [1, 0] 2173->2179 2175 entropy = 0.0 samples = 1 value = [0, 1] 2174->2175 2176 age <= 41.5 entropy = 1.0 samples = 4 value = [2, 2] 2174->2176 2177 entropy = 1.0 samples = 2 value = [1, 1] 2176->2177 2178 entropy = 1.0 samples = 2 value = [1, 1] 2176->2178 2182 workclass_Public <= 0.5 entropy = 0.996 samples = 110 value = [51, 59] 2181->2182 2281 workclass_Public <= 0.5 entropy = 0.987 samples = 53 value = [30, 23] 2181->2281 2183 hours-per-week <= 43.5 entropy = 0.998 samples = 105 value = [50, 55] 2182->2183 2278 age <= 51.5 entropy = 0.722 samples = 5 value = [1, 4] 2182->2278 2184 entropy = 0.0 samples = 2 value = [2, 0] 2183->2184 2185 hours-per-week <= 44.5 entropy = 0.997 samples = 103 value = [48, 55] 2183->2185 2186 entropy = 0.0 samples = 2 value = [0, 2] 2185->2186 2187 hours-per-week <= 45.5 entropy = 0.998 samples = 101 value = [48, 53] 2185->2187 2188 workclass_Self-emp <= 0.5 entropy = 0.934 samples = 20 value = [7, 13] 2187->2188 2207 race_Asian <= 0.5 entropy = 1.0 samples = 81 value = [41, 40] 2187->2207 2189 age <= 52.5 entropy = 0.989 samples = 16 value = [7, 9] 2188->2189 2206 entropy = 0.0 samples = 4 value = [0, 4] 2188->2206 2190 age <= 45.5 entropy = 0.971 samples = 15 value = [6, 9] 2189->2190 2205 entropy = 0.0 samples = 1 value = [1, 0] 2189->2205 2191 age <= 44.5 entropy = 0.971 samples = 5 value = [3, 2] 2190->2191 2194 age <= 46.5 entropy = 0.881 samples = 10 value = [3, 7] 2190->2194 2192 entropy = 0.918 samples = 3 value = [1, 2] 2191->2192 2193 entropy = 0.0 samples = 2 value = [2, 0] 2191->2193 2195 entropy = 0.0 samples = 1 value = [0, 1] 2194->2195 2196 age <= 50.5 entropy = 0.918 samples = 9 value = [3, 6] 2194->2196 2197 age <= 49.5 entropy = 0.954 samples = 8 value = [3, 5] 2196->2197 2204 entropy = 0.0 samples = 1 value = [0, 1] 2196->2204 2198 age <= 48.5 entropy = 0.918 samples = 6 value = [2, 4] 2197->2198 2203 entropy = 1.0 samples = 2 value = [1, 1] 2197->2203 2199 age <= 47.5 entropy = 0.971 samples = 5 value = [2, 3] 2198->2199 2202 entropy = 0.0 samples = 1 value = [0, 1] 2198->2202 2200 entropy = 0.918 samples = 3 value = [1, 2] 2199->2200 2201 entropy = 1.0 samples = 2 value = [1, 1] 2199->2201 2208 age <= 51.5 entropy = 1.0 samples = 80 value = [41, 39] 2207->2208 2277 entropy = 0.0 samples = 1 value = [0, 1] 2207->2277 2209 hours-per-week <= 46.5 entropy = 0.994 samples = 64 value = [35, 29] 2208->2209 2266 race_White <= 0.5 entropy = 0.954 samples = 16 value = [6, 10] 2208->2266 2210 entropy = 0.0 samples = 1 value = [1, 0] 2209->2210 2211 hours-per-week <= 47.5 entropy = 0.995 samples = 63 value = [34, 29] 2209->2211 2212 entropy = 0.0 samples = 1 value = [0, 1] 2211->2212 2213 age <= 50.5 entropy = 0.993 samples = 62 value = [34, 28] 2211->2213 2214 age <= 44.5 entropy = 0.998 samples = 55 value = [29, 26] 2213->2214 2259 hours-per-week <= 54.5 entropy = 0.863 samples = 7 value = [5, 2] 2213->2259 2215 hours-per-week <= 57.5 entropy = 0.811 samples = 4 value = [3, 1] 2214->2215 2218 hours-per-week <= 51.0 entropy = 1.0 samples = 51 value = [26, 25] 2214->2218 2216 entropy = 0.0 samples = 1 value = [1, 0] 2215->2216 2217 entropy = 0.918 samples = 3 value = [2, 1] 2215->2217 2219 age <= 46.5 entropy = 0.983 samples = 33 value = [19, 14] 2218->2219 2242 hours-per-week <= 59.0 entropy = 0.964 samples = 18 value = [7, 11] 2218->2242 2220 age <= 45.5 entropy = 0.65 samples = 6 value = [5, 1] 2219->2220 2223 age <= 47.5 entropy = 0.999 samples = 27 value = [14, 13] 2219->2223 2221 entropy = 1.0 samples = 2 value = [1, 1] 2220->2221 2222 entropy = 0.0 samples = 4 value = [4, 0] 2220->2222 2224 workclass_Private <= 0.5 entropy = 0.98 samples = 12 value = [5, 7] 2223->2224 2229 age <= 48.5 entropy = 0.971 samples = 15 value = [9, 6] 2223->2229 2225 entropy = 0.0 samples = 2 value = [0, 2] 2224->2225 2226 hours-per-week <= 49.0 entropy = 1.0 samples = 10 value = [5, 5] 2224->2226 2227 entropy = 0.918 samples = 3 value = [2, 1] 2226->2227 2228 entropy = 0.985 samples = 7 value = [3, 4] 2226->2228 2230 hours-per-week <= 49.0 entropy = 0.722 samples = 5 value = [4, 1] 2229->2230 2235 hours-per-week <= 49.0 entropy = 1.0 samples = 10 value = [5, 5] 2229->2235 2231 entropy = 0.0 samples = 1 value = [1, 0] 2230->2231 2232 workclass_Private <= 0.5 entropy = 0.811 samples = 4 value = [3, 1] 2230->2232 2233 entropy = 0.0 samples = 1 value = [1, 0] 2232->2233 2234 entropy = 0.918 samples = 3 value = [2, 1] 2232->2234 2236 entropy = 0.0 samples = 1 value = [0, 1] 2235->2236 2237 age <= 49.5 entropy = 0.991 samples = 9 value = [5, 4] 2235->2237 2238 entropy = 0.0 samples = 1 value = [1, 0] 2237->2238 2239 workclass_Self-emp <= 0.5 entropy = 1.0 samples = 8 value = [4, 4] 2237->2239 2240 entropy = 0.971 samples = 5 value = [2, 3] 2239->2240 2241 entropy = 0.918 samples = 3 value = [2, 1] 2239->2241 2243 age <= 46.5 entropy = 0.592 samples = 7 value = [1, 6] 2242->2243 2248 age <= 46.5 entropy = 0.994 samples = 11 value = [6, 5] 2242->2248 2244 entropy = 0.0 samples = 3 value = [0, 3] 2243->2244 2245 age <= 48.0 entropy = 0.811 samples = 4 value = [1, 3] 2243->2245 2246 entropy = 1.0 samples = 2 value = [1, 1] 2245->2246 2247 entropy = 0.0 samples = 2 value = [0, 2] 2245->2247 2249 workclass_Private <= 0.5 entropy = 0.918 samples = 3 value = [1, 2] 2248->2249 2252 age <= 47.5 entropy = 0.954 samples = 8 value = [5, 3] 2248->2252 2250 entropy = 0.0 samples = 1 value = [0, 1] 2249->2250 2251 entropy = 1.0 samples = 2 value = [1, 1] 2249->2251 2253 entropy = 0.0 samples = 2 value = [2, 0] 2252->2253 2254 workclass_Self-emp <= 0.5 entropy = 1.0 samples = 6 value = [3, 3] 2252->2254 2255 entropy = 0.0 samples = 2 value = [0, 2] 2254->2255 2256 age <= 48.5 entropy = 0.811 samples = 4 value = [3, 1] 2254->2256 2257 entropy = 0.0 samples = 1 value = [0, 1] 2256->2257 2258 entropy = 0.0 samples = 3 value = [3, 0] 2256->2258 2260 entropy = 0.0 samples = 3 value = [3, 0] 2259->2260 2261 workclass_Self-emp <= 0.5 entropy = 1.0 samples = 4 value = [2, 2] 2259->2261 2262 hours-per-week <= 57.5 entropy = 0.918 samples = 3 value = [1, 2] 2261->2262 2265 entropy = 0.0 samples = 1 value = [1, 0] 2261->2265 2263 entropy = 0.0 samples = 1 value = [0, 1] 2262->2263 2264 entropy = 1.0 samples = 2 value = [1, 1] 2262->2264 2267 entropy = 0.0 samples = 1 value = [1, 0] 2266->2267 2268 hours-per-week <= 49.0 entropy = 0.918 samples = 15 value = [5, 10] 2266->2268 2269 entropy = 0.0 samples = 1 value = [1, 0] 2268->2269 2270 hours-per-week <= 53.5 entropy = 0.863 samples = 14 value = [4, 10] 2268->2270 2271 age <= 52.5 entropy = 0.544 samples = 8 value = [1, 7] 2270->2271 2274 age <= 52.5 entropy = 1.0 samples = 6 value = [3, 3] 2270->2274 2272 entropy = 0.918 samples = 3 value = [1, 2] 2271->2272 2273 entropy = 0.0 samples = 5 value = [0, 5] 2271->2273 2275 entropy = 0.0 samples = 3 value = [0, 3] 2274->2275 2276 entropy = 0.0 samples = 3 value = [3, 0] 2274->2276 2279 entropy = 0.0 samples = 4 value = [0, 4] 2278->2279 2280 entropy = 0.0 samples = 1 value = [1, 0] 2278->2280 2282 hours-per-week <= 44.5 entropy = 0.995 samples = 50 value = [27, 23] 2281->2282 2333 entropy = 0.0 samples = 3 value = [3, 0] 2281->2333 2283 entropy = 0.0 samples = 2 value = [0, 2] 2282->2283 2284 race_White <= 0.5 entropy = 0.989 samples = 48 value = [27, 21] 2282->2284 2285 hours-per-week <= 56.0 entropy = 0.722 samples = 5 value = [4, 1] 2284->2285 2288 hours-per-week <= 57.5 entropy = 0.996 samples = 43 value = [23, 20] 2284->2288 2286 entropy = 0.0 samples = 4 value = [4, 0] 2285->2286 2287 entropy = 0.0 samples = 1 value = [0, 1] 2285->2287 2289 hours-per-week <= 51.0 entropy = 0.999 samples = 31 value = [15, 16] 2288->2289 2322 age <= 59.5 entropy = 0.918 samples = 12 value = [8, 4] 2288->2322 2290 hours-per-week <= 46.5 entropy = 0.995 samples = 24 value = [13, 11] 2289->2290 2317 workclass_Private <= 0.5 entropy = 0.863 samples = 7 value = [2, 5] 2289->2317 2291 age <= 57.0 entropy = 0.971 samples = 5 value = [2, 3] 2290->2291 2296 age <= 57.5 entropy = 0.982 samples = 19 value = [11, 8] 2290->2296 2292 age <= 55.5 entropy = 0.918 samples = 3 value = [2, 1] 2291->2292 2295 entropy = 0.0 samples = 2 value = [0, 2] 2291->2295 2293 entropy = 1.0 samples = 2 value = [1, 1] 2292->2293 2294 entropy = 0.0 samples = 1 value = [1, 0] 2292->2294 2297 hours-per-week <= 49.0 entropy = 0.991 samples = 9 value = [4, 5] 2296->2297 2308 hours-per-week <= 49.0 entropy = 0.881 samples = 10 value = [7, 3] 2296->2308 2298 entropy = 0.0 samples = 1 value = [0, 1] 2297->2298 2299 age <= 55.5 entropy = 1.0 samples = 8 value = [4, 4] 2297->2299 2300 workclass_Self-emp <= 0.5 entropy = 0.918 samples = 3 value = [1, 2] 2299->2300 2303 workclass_Private <= 0.5 entropy = 0.971 samples = 5 value = [3, 2] 2299->2303 2301 entropy = 1.0 samples = 2 value = [1, 1] 2300->2301 2302 entropy = 0.0 samples = 1 value = [0, 1] 2300->2302 2304 entropy = 1.0 samples = 2 value = [1, 1] 2303->2304 2305 age <= 56.5 entropy = 0.918 samples = 3 value = [2, 1] 2303->2305 2306 entropy = 0.0 samples = 2 value = [2, 0] 2305->2306 2307 entropy = 0.0 samples = 1 value = [0, 1] 2305->2307 2309 entropy = 0.0 samples = 3 value = [3, 0] 2308->2309 2310 workclass_Private <= 0.5 entropy = 0.985 samples = 7 value = [4, 3] 2308->2310 2311 age <= 59.0 entropy = 0.811 samples = 4 value = [3, 1] 2310->2311 2314 age <= 59.5 entropy = 0.918 samples = 3 value = [1, 2] 2310->2314 2312 entropy = 0.0 samples = 2 value = [2, 0] 2311->2312 2313 entropy = 1.0 samples = 2 value = [1, 1] 2311->2313 2315 entropy = 0.0 samples = 1 value = [0, 1] 2314->2315 2316 entropy = 1.0 samples = 2 value = [1, 1] 2314->2316 2318 entropy = 0.0 samples = 4 value = [0, 4] 2317->2318 2319 age <= 57.0 entropy = 0.918 samples = 3 value = [2, 1] 2317->2319 2320 entropy = 0.0 samples = 1 value = [0, 1] 2319->2320 2321 entropy = 0.0 samples = 2 value = [2, 0] 2319->2321 2323 workclass_Private <= 0.5 entropy = 0.722 samples = 10 value = [8, 2] 2322->2323 2332 entropy = 0.0 samples = 2 value = [0, 2] 2322->2332 2324 age <= 55.5 entropy = 0.918 samples = 6 value = [4, 2] 2323->2324 2331 entropy = 0.0 samples = 4 value = [4, 0] 2323->2331 2325 entropy = 0.0 samples = 1 value = [1, 0] 2324->2325 2326 age <= 57.0 entropy = 0.971 samples = 5 value = [3, 2] 2324->2326 2327 entropy = 0.0 samples = 1 value = [0, 1] 2326->2327 2328 age <= 58.5 entropy = 0.811 samples = 4 value = [3, 1] 2326->2328 2329 entropy = 0.918 samples = 3 value = [2, 1] 2328->2329 2330 entropy = 0.0 samples = 1 value = [1, 0] 2328->2330 2336 age <= 39.5 entropy = 0.938 samples = 31 value = [20, 11] 2335->2336 2363 entropy = 0.0 samples = 9 value = [9, 0] 2335->2363 2337 hours-per-week <= 91.5 entropy = 0.544 samples = 8 value = [7, 1] 2336->2337 2340 race_Asian <= 0.5 entropy = 0.988 samples = 23 value = [13, 10] 2336->2340 2338 entropy = 0.0 samples = 6 value = [6, 0] 2337->2338 2339 entropy = 1.0 samples = 2 value = [1, 1] 2337->2339 2341 age <= 43.5 entropy = 0.998 samples = 21 value = [11, 10] 2340->2341 2362 entropy = 0.0 samples = 2 value = [2, 0] 2340->2362 2342 entropy = 0.0 samples = 2 value = [0, 2] 2341->2342 2343 race_White <= 0.5 entropy = 0.982 samples = 19 value = [11, 8] 2341->2343 2344 entropy = 0.0 samples = 1 value = [0, 1] 2343->2344 2345 workclass_Public <= 0.5 entropy = 0.964 samples = 18 value = [11, 7] 2343->2345 2346 hours-per-week <= 86.5 entropy = 0.997 samples = 15 value = [8, 7] 2345->2346 2361 entropy = 0.0 samples = 3 value = [3, 0] 2345->2361 2347 hours-per-week <= 72.5 entropy = 0.994 samples = 11 value = [5, 6] 2346->2347 2358 age <= 50.0 entropy = 0.811 samples = 4 value = [3, 1] 2346->2358 2348 workclass_Private <= 0.5 entropy = 0.954 samples = 8 value = [5, 3] 2347->2348 2357 entropy = 0.0 samples = 3 value = [0, 3] 2347->2357 2349 entropy = 0.0 samples = 2 value = [2, 0] 2348->2349 2350 age <= 47.0 entropy = 1.0 samples = 6 value = [3, 3] 2348->2350 2351 hours-per-week <= 67.5 entropy = 0.971 samples = 5 value = [2, 3] 2350->2351 2356 entropy = 0.0 samples = 1 value = [1, 0] 2350->2356 2352 age <= 45.0 entropy = 0.918 samples = 3 value = [1, 2] 2351->2352 2355 entropy = 1.0 samples = 2 value = [1, 1] 2351->2355 2353 entropy = 0.0 samples = 2 value = [0, 2] 2352->2353 2354 entropy = 0.0 samples = 1 value = [1, 0] 2352->2354 2359 entropy = 0.0 samples = 3 value = [3, 0] 2358->2359 2360 entropy = 0.0 samples = 1 value = [0, 1] 2358->2360 2366 entropy = 0.0 samples = 11 value = [11, 0] 2365->2366 2367 entropy = 0.0 samples = 1 value = [0, 1] 2365->2367 2369 age <= 47.5 entropy = 0.996 samples = 375 value = [202, 173] 2368->2369 2646 workclass_Private <= 0.5 entropy = 0.994 samples = 267 value = [121, 146] 2368->2646 2370 hours-per-week <= 39.0 entropy = 0.983 samples = 231 value = [133, 98] 2369->2370 2537 race_Asian <= 0.5 entropy = 0.999 samples = 144 value = [69, 75] 2369->2537 2371 workclass_Private <= 0.5 entropy = 0.779 samples = 13 value = [3, 10] 2370->2371 2380 education <= 10.5 entropy = 0.973 samples = 218 value = [130, 88] 2370->2380 2372 entropy = 0.0 samples = 5 value = [0, 5] 2371->2372 2373 age <= 37.5 entropy = 0.954 samples = 8 value = [3, 5] 2371->2373 2374 entropy = 0.0 samples = 1 value = [1, 0] 2373->2374 2375 age <= 46.0 entropy = 0.863 samples = 7 value = [2, 5] 2373->2375 2376 sex_Male <= 0.5 entropy = 0.65 samples = 6 value = [1, 5] 2375->2376 2379 entropy = 0.0 samples = 1 value = [1, 0] 2375->2379 2377 entropy = 0.0 samples = 4 value = [0, 4] 2376->2377 2378 entropy = 1.0 samples = 2 value = [1, 1] 2376->2378 2381 age <= 40.5 entropy = 0.942 samples = 170 value = [109, 61] 2380->2381 2500 age <= 38.225 entropy = 0.989 samples = 48 value = [21, 27] 2380->2500 2382 age <= 38.225 entropy = 0.791 samples = 59 value = [45, 14] 2381->2382 2429 hours-per-week <= 42.5 entropy = 0.983 samples = 111 value = [64, 47] 2381->2429 2383 hours-per-week <= 41.0 entropy = 0.907 samples = 31 value = [21, 10] 2382->2383 2414 sex_Male <= 0.5 entropy = 0.592 samples = 28 value = [24, 4] 2382->2414 2384 race_Black <= 0.5 entropy = 0.863 samples = 28 value = [20, 8] 2383->2384 2411 hours-per-week <= 42.5 entropy = 0.918 samples = 3 value = [1, 2] 2383->2411 2385 age <= 37.5 entropy = 0.811 samples = 24 value = [18, 6] 2384->2385 2406 sex_Female <= 0.5 entropy = 1.0 samples = 4 value = [2, 2] 2384->2406 2386 workclass_Public <= 0.5 entropy = 0.696 samples = 16 value = [13, 3] 2385->2386 2397 workclass_Self-emp <= 0.5 entropy = 0.954 samples = 8 value = [5, 3] 2385->2397 2387 age <= 36.5 entropy = 0.503 samples = 9 value = [8, 1] 2386->2387 2392 sex_Female <= 0.5 entropy = 0.863 samples = 7 value = [5, 2] 2386->2392 2388 entropy = 0.0 samples = 4 value = [4, 0] 2387->2388 2389 workclass_Private <= 0.5 entropy = 0.722 samples = 5 value = [4, 1] 2387->2389 2390 entropy = 0.0 samples = 1 value = [1, 0] 2389->2390 2391 entropy = 0.811 samples = 4 value = [3, 1] 2389->2391 2393 age <= 36.5 entropy = 0.918 samples = 6 value = [4, 2] 2392->2393 2396 entropy = 0.0 samples = 1 value = [1, 0] 2392->2396 2394 entropy = 0.0 samples = 1 value = [0, 1] 2393->2394 2395 entropy = 0.722 samples = 5 value = [4, 1] 2393->2395 2398 sex_Female <= 0.5 entropy = 0.863 samples = 7 value = [5, 2] 2397->2398 2405 entropy = 0.0 samples = 1 value = [0, 1] 2397->2405 2399 workclass_Private <= 0.5 entropy = 0.65 samples = 6 value = [5, 1] 2398->2399 2404 entropy = 0.0 samples = 1 value = [0, 1] 2398->2404 2400 entropy = 0.0 samples = 1 value = [1, 0] 2399->2400 2401 race_White <= 0.5 entropy = 0.722 samples = 5 value = [4, 1] 2399->2401 2402 entropy = 0.0 samples = 1 value = [1, 0] 2401->2402 2403 entropy = 0.811 samples = 4 value = [3, 1] 2401->2403 2407 entropy = 0.0 samples = 1 value = [0, 1] 2406->2407 2408 age <= 37.0 entropy = 0.918 samples = 3 value = [2, 1] 2406->2408 2409 entropy = 0.0 samples = 1 value = [0, 1] 2408->2409 2410 entropy = 0.0 samples = 2 value = [2, 0] 2408->2410 2412 entropy = 0.0 samples = 2 value = [0, 2] 2411->2412 2413 entropy = 0.0 samples = 1 value = [1, 0] 2411->2413 2415 age <= 39.225 entropy = 0.918 samples = 3 value = [2, 1] 2414->2415 2418 hours-per-week <= 41.0 entropy = 0.529 samples = 25 value = [22, 3] 2414->2418 2416 entropy = 0.0 samples = 1 value = [1, 0] 2415->2416 2417 entropy = 1.0 samples = 2 value = [1, 1] 2415->2417 2419 age <= 38.725 entropy = 0.439 samples = 22 value = [20, 2] 2418->2419 2426 hours-per-week <= 42.5 entropy = 0.918 samples = 3 value = [2, 1] 2418->2426 2420 workclass_Public <= 0.5 entropy = 0.722 samples = 10 value = [8, 2] 2419->2420 2425 entropy = 0.0 samples = 12 value = [12, 0] 2419->2425 2421 race_White <= 0.5 entropy = 0.811 samples = 8 value = [6, 2] 2420->2421 2424 entropy = 0.0 samples = 2 value = [2, 0] 2420->2424 2422 entropy = 0.0 samples = 1 value = [1, 0] 2421->2422 2423 entropy = 0.863 samples = 7 value = [5, 2] 2421->2423 2427 entropy = 0.0 samples = 1 value = [0, 1] 2426->2427 2428 entropy = 0.0 samples = 2 value = [2, 0] 2426->2428 2430 workclass_Public <= 0.5 entropy = 0.981 samples = 110 value = [64, 46] 2429->2430 2499 entropy = 0.0 samples = 1 value = [0, 1] 2429->2499 2431 age <= 44.5 entropy = 0.964 samples = 85 value = [52, 33] 2430->2431 2480 age <= 44.5 entropy = 0.999 samples = 25 value = [12, 13] 2430->2480 2432 race_Asian <= 0.5 entropy = 0.993 samples = 51 value = [28, 23] 2431->2432 2463 race_Black <= 0.5 entropy = 0.874 samples = 34 value = [24, 10] 2431->2463 2433 hours-per-week <= 41.0 entropy = 0.99 samples = 50 value = [28, 22] 2432->2433 2462 entropy = 0.0 samples = 1 value = [0, 1] 2432->2462 2434 age <= 42.5 entropy = 0.992 samples = 49 value = [27, 22] 2433->2434 2461 entropy = 0.0 samples = 1 value = [1, 0] 2433->2461 2435 sex_Female <= 0.5 entropy = 0.998 samples = 21 value = [10, 11] 2434->2435 2448 sex_Male <= 0.5 entropy = 0.967 samples = 28 value = [17, 11] 2434->2448 2436 race_Black <= 0.5 entropy = 1.0 samples = 20 value = [10, 10] 2435->2436 2447 entropy = 0.0 samples = 1 value = [0, 1] 2435->2447 2437 workclass_Private <= 0.5 entropy = 0.998 samples = 17 value = [9, 8] 2436->2437 2444 age <= 41.5 entropy = 0.918 samples = 3 value = [1, 2] 2436->2444 2438 age <= 41.5 entropy = 0.918 samples = 3 value = [1, 2] 2437->2438 2441 age <= 41.5 entropy = 0.985 samples = 14 value = [8, 6] 2437->2441 2439 entropy = 0.0 samples = 1 value = [0, 1] 2438->2439 2440 entropy = 1.0 samples = 2 value = [1, 1] 2438->2440 2442 entropy = 1.0 samples = 6 value = [3, 3] 2441->2442 2443 entropy = 0.954 samples = 8 value = [5, 3] 2441->2443 2445 entropy = 0.0 samples = 1 value = [1, 0] 2444->2445 2446 entropy = 0.0 samples = 2 value = [0, 2] 2444->2446 2449 entropy = 0.0 samples = 3 value = [3, 0] 2448->2449 2450 age <= 43.5 entropy = 0.99 samples = 25 value = [14, 11] 2448->2450 2451 race_Black <= 0.5 entropy = 1.0 samples = 14 value = [7, 7] 2450->2451 2456 workclass_Private <= 0.5 entropy = 0.946 samples = 11 value = [7, 4] 2450->2456 2452 workclass_Self-emp <= 0.5 entropy = 0.996 samples = 13 value = [7, 6] 2451->2452 2455 entropy = 0.0 samples = 1 value = [0, 1] 2451->2455 2453 entropy = 1.0 samples = 12 value = [6, 6] 2452->2453 2454 entropy = 0.0 samples = 1 value = [1, 0] 2452->2454 2457 entropy = 0.0 samples = 1 value = [0, 1] 2456->2457 2458 race_Black <= 0.5 entropy = 0.881 samples = 10 value = [7, 3] 2456->2458 2459 entropy = 0.918 samples = 9 value = [6, 3] 2458->2459 2460 entropy = 0.0 samples = 1 value = [1, 0] 2458->2460 2464 race_White <= 0.5 entropy = 0.784 samples = 30 value = [23, 7] 2463->2464 2477 age <= 46.5 entropy = 0.811 samples = 4 value = [1, 3] 2463->2477 2465 entropy = 0.0 samples = 4 value = [4, 0] 2464->2465 2466 age <= 46.5 entropy = 0.84 samples = 26 value = [19, 7] 2464->2466 2467 sex_Female <= 0.5 entropy = 0.937 samples = 17 value = [11, 6] 2466->2467 2474 workclass_Self-emp <= 0.5 entropy = 0.503 samples = 9 value = [8, 1] 2466->2474 2468 age <= 45.5 entropy = 0.918 samples = 15 value = [10, 5] 2467->2468 2473 entropy = 1.0 samples = 2 value = [1, 1] 2467->2473 2469 workclass_Self-emp <= 0.5 entropy = 0.985 samples = 7 value = [4, 3] 2468->2469 2472 entropy = 0.811 samples = 8 value = [6, 2] 2468->2472 2470 entropy = 0.971 samples = 5 value = [3, 2] 2469->2470 2471 entropy = 1.0 samples = 2 value = [1, 1] 2469->2471 2475 entropy = 0.592 samples = 7 value = [6, 1] 2474->2475 2476 entropy = 0.0 samples = 2 value = [2, 0] 2474->2476 2478 entropy = 1.0 samples = 2 value = [1, 1] 2477->2478 2479 entropy = 0.0 samples = 2 value = [0, 2] 2477->2479 2481 age <= 43.5 entropy = 0.989 samples = 16 value = [9, 7] 2480->2481 2492 age <= 46.5 entropy = 0.918 samples = 9 value = [3, 6] 2480->2492 2482 race_White <= 0.5 entropy = 1.0 samples = 14 value = [7, 7] 2481->2482 2491 entropy = 0.0 samples = 2 value = [2, 0] 2481->2491 2483 entropy = 0.0 samples = 1 value = [1, 0] 2482->2483 2484 age <= 41.5 entropy = 0.996 samples = 13 value = [6, 7] 2482->2484 2485 entropy = 0.971 samples = 5 value = [3, 2] 2484->2485 2486 sex_Male <= 0.5 entropy = 0.954 samples = 8 value = [3, 5] 2484->2486 2487 entropy = 1.0 samples = 2 value = [1, 1] 2486->2487 2488 age <= 42.5 entropy = 0.918 samples = 6 value = [2, 4] 2486->2488 2489 entropy = 0.0 samples = 2 value = [0, 2] 2488->2489 2490 entropy = 1.0 samples = 4 value = [2, 2] 2488->2490 2493 age <= 45.5 entropy = 0.722 samples = 5 value = [1, 4] 2492->2493 2496 race_Black <= 0.5 entropy = 1.0 samples = 4 value = [2, 2] 2492->2496 2494 entropy = 0.811 samples = 4 value = [1, 3] 2493->2494 2495 entropy = 0.0 samples = 1 value = [0, 1] 2493->2495 2497 entropy = 0.918 samples = 3 value = [2, 1] 2496->2497 2498 entropy = 0.0 samples = 1 value = [0, 1] 2496->2498 2501 age <= 37.5 entropy = 0.503 samples = 9 value = [1, 8] 2500->2501 2506 sex_Male <= 0.5 entropy = 1.0 samples = 39 value = [20, 19] 2500->2506 2502 age <= 36.5 entropy = 0.65 samples = 6 value = [1, 5] 2501->2502 2505 entropy = 0.0 samples = 3 value = [0, 3] 2501->2505 2503 entropy = 0.0 samples = 2 value = [0, 2] 2502->2503 2504 entropy = 0.811 samples = 4 value = [1, 3] 2502->2504 2507 entropy = 0.0 samples = 3 value = [0, 3] 2506->2507 2508 race_Asian <= 0.5 entropy = 0.991 samples = 36 value = [20, 16] 2506->2508 2509 workclass_Self-emp <= 0.5 entropy = 0.977 samples = 34 value = [20, 14] 2508->2509 2536 entropy = 0.0 samples = 2 value = [0, 2] 2508->2536 2510 age <= 46.5 entropy = 0.967 samples = 33 value = [20, 13] 2509->2510 2535 entropy = 0.0 samples = 1 value = [0, 1] 2509->2535 2511 age <= 45.5 entropy = 0.94 samples = 28 value = [18, 10] 2510->2511 2532 workclass_Public <= 0.5 entropy = 0.971 samples = 5 value = [2, 3] 2510->2532 2512 age <= 41.5 entropy = 0.971 samples = 25 value = [15, 10] 2511->2512 2531 entropy = 0.0 samples = 3 value = [3, 0] 2511->2531 2513 workclass_Public <= 0.5 entropy = 0.89 samples = 13 value = [9, 4] 2512->2513 2522 age <= 43.0 entropy = 1.0 samples = 12 value = [6, 6] 2512->2522 2514 race_White <= 0.5 entropy = 0.946 samples = 11 value = [7, 4] 2513->2514 2521 entropy = 0.0 samples = 2 value = [2, 0] 2513->2521 2515 entropy = 0.0 samples = 1 value = [1, 0] 2514->2515 2516 age <= 40.5 entropy = 0.971 samples = 10 value = [6, 4] 2514->2516 2517 age <= 39.225 entropy = 0.954 samples = 8 value = [5, 3] 2516->2517 2520 entropy = 1.0 samples = 2 value = [1, 1] 2516->2520 2518 entropy = 0.918 samples = 3 value = [2, 1] 2517->2518 2519 entropy = 0.971 samples = 5 value = [3, 2] 2517->2519 2523 entropy = 0.0 samples = 2 value = [0, 2] 2522->2523 2524 workclass_Public <= 0.5 entropy = 0.971 samples = 10 value = [6, 4] 2522->2524 2525 age <= 44.5 entropy = 0.954 samples = 8 value = [5, 3] 2524->2525 2530 entropy = 1.0 samples = 2 value = [1, 1] 2524->2530 2526 race_Black <= 0.5 entropy = 0.918 samples = 6 value = [4, 2] 2525->2526 2529 entropy = 1.0 samples = 2 value = [1, 1] 2525->2529 2527 entropy = 0.918 samples = 3 value = [2, 1] 2526->2527 2528 entropy = 0.918 samples = 3 value = [2, 1] 2526->2528 2533 entropy = 1.0 samples = 4 value = [2, 2] 2532->2533 2534 entropy = 0.0 samples = 1 value = [0, 1] 2532->2534 2538 hours-per-week <= 37.5 entropy = 1.0 samples = 139 value = [69, 70] 2537->2538 2645 entropy = 0.0 samples = 5 value = [0, 5] 2537->2645 2539 age <= 50.5 entropy = 0.722 samples = 10 value = [8, 2] 2538->2539 2546 age <= 59.5 entropy = 0.998 samples = 129 value = [61, 68] 2538->2546 2540 sex_Male <= 0.5 entropy = 1.0 samples = 4 value = [2, 2] 2539->2540 2545 entropy = 0.0 samples = 6 value = [6, 0] 2539->2545 2541 entropy = 0.0 samples = 1 value = [1, 0] 2540->2541 2542 age <= 49.0 entropy = 0.918 samples = 3 value = [1, 2] 2540->2542 2543 entropy = 0.0 samples = 1 value = [0, 1] 2542->2543 2544 entropy = 1.0 samples = 2 value = [1, 1] 2542->2544 2547 sex_Male <= 0.5 entropy = 0.994 samples = 117 value = [53, 64] 2546->2547 2634 sex_Female <= 0.5 entropy = 0.918 samples = 12 value = [8, 4] 2546->2634 2548 education <= 10.5 entropy = 0.966 samples = 23 value = [14, 9] 2547->2548 2573 race_White <= 0.5 entropy = 0.979 samples = 94 value = [39, 55] 2547->2573 2549 race_Black <= 0.5 entropy = 0.998 samples = 19 value = [10, 9] 2548->2549 2572 entropy = 0.0 samples = 4 value = [4, 0] 2548->2572 2550 age <= 52.0 entropy = 0.998 samples = 17 value = [8, 9] 2549->2550 2571 entropy = 0.0 samples = 2 value = [2, 0] 2549->2571 2551 workclass_Public <= 0.5 entropy = 0.918 samples = 9 value = [3, 6] 2550->2551 2562 workclass_Public <= 0.5 entropy = 0.954 samples = 8 value = [5, 3] 2550->2562 2552 age <= 48.5 entropy = 0.954 samples = 8 value = [3, 5] 2551->2552 2561 entropy = 0.0 samples = 1 value = [0, 1] 2551->2561 2553 entropy = 0.0 samples = 1 value = [1, 0] 2552->2553 2554 age <= 49.5 entropy = 0.863 samples = 7 value = [2, 5] 2552->2554 2555 entropy = 0.0 samples = 2 value = [0, 2] 2554->2555 2556 hours-per-week <= 39.0 entropy = 0.971 samples = 5 value = [2, 3] 2554->2556 2557 entropy = 0.0 samples = 1 value = [0, 1] 2556->2557 2558 age <= 50.5 entropy = 1.0 samples = 4 value = [2, 2] 2556->2558 2559 entropy = 0.0 samples = 1 value = [1, 0] 2558->2559 2560 entropy = 0.918 samples = 3 value = [1, 2] 2558->2560 2563 age <= 56.5 entropy = 1.0 samples = 6 value = [3, 3] 2562->2563 2570 entropy = 0.0 samples = 2 value = [2, 0] 2562->2570 2564 age <= 53.5 entropy = 0.971 samples = 5 value = [3, 2] 2563->2564 2569 entropy = 0.0 samples = 1 value = [0, 1] 2563->2569 2565 entropy = 1.0 samples = 2 value = [1, 1] 2564->2565 2566 age <= 54.5 entropy = 0.918 samples = 3 value = [2, 1] 2564->2566 2567 entropy = 0.0 samples = 1 value = [1, 0] 2566->2567 2568 entropy = 1.0 samples = 2 value = [1, 1] 2566->2568 2574 entropy = 0.0 samples = 5 value = [0, 5] 2573->2574 2575 age <= 54.5 entropy = 0.989 samples = 89 value = [39, 50] 2573->2575 2576 workclass_Self-emp <= 0.5 entropy = 0.999 samples = 66 value = [32, 34] 2575->2576 2613 workclass_Self-emp <= 0.5 entropy = 0.887 samples = 23 value = [7, 16] 2575->2613 2577 hours-per-week <= 41.0 entropy = 0.993 samples = 62 value = [28, 34] 2576->2577 2612 entropy = 0.0 samples = 4 value = [4, 0] 2576->2612 2578 age <= 49.5 entropy = 0.987 samples = 60 value = [26, 34] 2577->2578 2611 entropy = 0.0 samples = 2 value = [2, 0] 2577->2611 2579 education <= 10.5 entropy = 0.787 samples = 17 value = [4, 13] 2578->2579 2588 education <= 10.5 entropy = 1.0 samples = 43 value = [22, 21] 2578->2588 2580 age <= 48.5 entropy = 0.811 samples = 16 value = [4, 12] 2579->2580 2587 entropy = 0.0 samples = 1 value = [0, 1] 2579->2587 2581 workclass_Public <= 0.5 entropy = 0.65 samples = 6 value = [1, 5] 2580->2581 2584 workclass_Public <= 0.5 entropy = 0.881 samples = 10 value = [3, 7] 2580->2584 2582 entropy = 0.722 samples = 5 value = [1, 4] 2581->2582 2583 entropy = 0.0 samples = 1 value = [0, 1] 2581->2583 2585 entropy = 0.811 samples = 4 value = [1, 3] 2584->2585 2586 entropy = 0.918 samples = 6 value = [2, 4] 2584->2586 2589 age <= 52.5 entropy = 0.997 samples = 32 value = [15, 17] 2588->2589 2604 age <= 51.5 entropy = 0.946 samples = 11 value = [7, 4] 2588->2604 2590 age <= 50.5 entropy = 0.985 samples = 21 value = [9, 12] 2589->2590 2599 age <= 53.5 entropy = 0.994 samples = 11 value = [6, 5] 2589->2599 2591 workclass_Public <= 0.5 entropy = 0.918 samples = 3 value = [1, 2] 2590->2591 2594 workclass_Private <= 0.5 entropy = 0.991 samples = 18 value = [8, 10] 2590->2594 2592 entropy = 1.0 samples = 2 value = [1, 1] 2591->2592 2593 entropy = 0.0 samples = 1 value = [0, 1] 2591->2593 2595 entropy = 0.0 samples = 1 value = [1, 0] 2594->2595 2596 age <= 51.5 entropy = 0.977 samples = 17 value = [7, 10] 2594->2596 2597 entropy = 0.971 samples = 10 value = [4, 6] 2596->2597 2598 entropy = 0.985 samples = 7 value = [3, 4] 2596->2598 2600 entropy = 0.971 samples = 5 value = [3, 2] 2599->2600 2601 workclass_Private <= 0.5 entropy = 1.0 samples = 6 value = [3, 3] 2599->2601 2602 entropy = 1.0 samples = 2 value = [1, 1] 2601->2602 2603 entropy = 1.0 samples = 4 value = [2, 2] 2601->2603 2605 age <= 50.5 entropy = 1.0 samples = 6 value = [3, 3] 2604->2605 2608 age <= 52.5 entropy = 0.722 samples = 5 value = [4, 1] 2604->2608 2606 entropy = 0.0 samples = 1 value = [1, 0] 2605->2606 2607 entropy = 0.971 samples = 5 value = [2, 3] 2605->2607 2609 entropy = 0.0 samples = 3 value = [3, 0] 2608->2609 2610 entropy = 1.0 samples = 2 value = [1, 1] 2608->2610 2614 hours-per-week <= 41.5 entropy = 0.918 samples = 21 value = [7, 14] 2613->2614 2633 entropy = 0.0 samples = 2 value = [0, 2] 2613->2633 2615 age <= 56.5 entropy = 0.934 samples = 20 value = [7, 13] 2614->2615 2632 entropy = 0.0 samples = 1 value = [0, 1] 2614->2632 2616 age <= 55.5 entropy = 0.722 samples = 5 value = [1, 4] 2615->2616 2619 workclass_Public <= 0.5 entropy = 0.971 samples = 15 value = [6, 9] 2615->2619 2617 entropy = 0.918 samples = 3 value = [1, 2] 2616->2617 2618 entropy = 0.0 samples = 2 value = [0, 2] 2616->2618 2620 age <= 58.5 entropy = 0.961 samples = 13 value = [5, 8] 2619->2620 2631 entropy = 1.0 samples = 2 value = [1, 1] 2619->2631 2621 education <= 10.5 entropy = 0.991 samples = 9 value = [4, 5] 2620->2621 2628 education <= 10.5 entropy = 0.811 samples = 4 value = [1, 3] 2620->2628 2622 age <= 57.5 entropy = 0.971 samples = 5 value = [2, 3] 2621->2622 2625 age <= 57.5 entropy = 1.0 samples = 4 value = [2, 2] 2621->2625 2623 entropy = 0.811 samples = 4 value = [1, 3] 2622->2623 2624 entropy = 0.0 samples = 1 value = [1, 0] 2622->2624 2626 entropy = 0.0 samples = 1 value = [1, 0] 2625->2626 2627 entropy = 0.918 samples = 3 value = [1, 2] 2625->2627 2629 entropy = 0.918 samples = 3 value = [1, 2] 2628->2629 2630 entropy = 0.0 samples = 1 value = [0, 1] 2628->2630 2635 age <= 60.5 entropy = 0.845 samples = 11 value = [8, 3] 2634->2635 2644 entropy = 0.0 samples = 1 value = [0, 1] 2634->2644 2636 entropy = 0.0 samples = 2 value = [2, 0] 2635->2636 2637 education <= 10.5 entropy = 0.918 samples = 9 value = [6, 3] 2635->2637 2638 age <= 61.5 entropy = 0.811 samples = 8 value = [6, 2] 2637->2638 2643 entropy = 0.0 samples = 1 value = [0, 1] 2637->2643 2639 workclass_Self-emp <= 0.5 entropy = 0.918 samples = 6 value = [4, 2] 2638->2639 2642 entropy = 0.0 samples = 2 value = [2, 0] 2638->2642 2640 entropy = 0.971 samples = 5 value = [3, 2] 2639->2640 2641 entropy = 0.0 samples = 1 value = [1, 0] 2639->2641 2647 sex_Male <= 0.5 entropy = 0.997 samples = 111 value = [59, 52] 2646->2647 2734 age <= 60.5 entropy = 0.969 samples = 156 value = [62, 94] 2646->2734 2648 entropy = 0.0 samples = 4 value = [4, 0] 2647->2648 2649 age <= 57.5 entropy = 0.999 samples = 107 value = [55, 52] 2647->2649 2650 hours-per-week <= 58.5 entropy = 0.995 samples = 100 value = [54, 46] 2649->2650 2731 workclass_Self-emp <= 0.5 entropy = 0.592 samples = 7 value = [1, 6] 2649->2731 2651 hours-per-week <= 53.0 entropy = 0.992 samples = 56 value = [25, 31] 2650->2651 2690 hours-per-week <= 94.5 entropy = 0.926 samples = 44 value = [29, 15] 2650->2690 2652 age <= 49.5 entropy = 0.999 samples = 46 value = [24, 22] 2651->2652 2687 age <= 55.5 entropy = 0.469 samples = 10 value = [1, 9] 2651->2687 2653 age <= 48.5 entropy = 0.981 samples = 31 value = [13, 18] 2652->2653 2672 age <= 53.5 entropy = 0.837 samples = 15 value = [11, 4] 2652->2672 2654 age <= 43.5 entropy = 0.999 samples = 27 value = [13, 14] 2653->2654 2671 entropy = 0.0 samples = 4 value = [0, 4] 2653->2671 2655 hours-per-week <= 49.0 entropy = 0.977 samples = 17 value = [10, 7] 2654->2655 2664 hours-per-week <= 46.5 entropy = 0.881 samples = 10 value = [3, 7] 2654->2664 2656 hours-per-week <= 44.5 entropy = 0.65 samples = 6 value = [1, 5] 2655->2656 2659 age <= 37.725 entropy = 0.684 samples = 11 value = [9, 2] 2655->2659 2657 entropy = 0.0 samples = 1 value = [1, 0] 2656->2657 2658 entropy = 0.0 samples = 5 value = [0, 5] 2656->2658 2660 entropy = 1.0 samples = 2 value = [1, 1] 2659->2660 2661 age <= 42.5 entropy = 0.503 samples = 9 value = [8, 1] 2659->2661 2662 entropy = 0.0 samples = 5 value = [5, 0] 2661->2662 2663 entropy = 0.811 samples = 4 value = [3, 1] 2661->2663 2665 entropy = 0.0 samples = 2 value = [2, 0] 2664->2665 2666 age <= 46.0 entropy = 0.544 samples = 8 value = [1, 7] 2664->2666 2667 entropy = 0.0 samples = 5 value = [0, 5] 2666->2667 2668 race_White <= 0.5 entropy = 0.918 samples = 3 value = [1, 2] 2666->2668 2669 entropy = 0.0 samples = 1 value = [0, 1] 2668->2669 2670 entropy = 1.0 samples = 2 value = [1, 1] 2668->2670 2673 age <= 52.5 entropy = 0.592 samples = 7 value = [6, 1] 2672->2673 2678 hours-per-week <= 49.0 entropy = 0.954 samples = 8 value = [5, 3] 2672->2678 2674 hours-per-week <= 49.0 entropy = 0.811 samples = 4 value = [3, 1] 2673->2674 2677 entropy = 0.0 samples = 3 value = [3, 0] 2673->2677 2675 entropy = 0.0 samples = 2 value = [2, 0] 2674->2675 2676 entropy = 1.0 samples = 2 value = [1, 1] 2674->2676 2679 age <= 55.0 entropy = 0.918 samples = 3 value = [1, 2] 2678->2679 2682 age <= 55.0 entropy = 0.722 samples = 5 value = [4, 1] 2678->2682 2680 entropy = 0.0 samples = 2 value = [0, 2] 2679->2680 2681 entropy = 0.0 samples = 1 value = [1, 0] 2679->2681 2683 entropy = 0.0 samples = 2 value = [2, 0] 2682->2683 2684 age <= 56.5 entropy = 0.918 samples = 3 value = [2, 1] 2682->2684 2685 entropy = 1.0 samples = 2 value = [1, 1] 2684->2685 2686 entropy = 0.0 samples = 1 value = [1, 0] 2684->2686 2688 entropy = 0.0 samples = 9 value = [0, 9] 2687->2688 2689 entropy = 0.0 samples = 1 value = [1, 0] 2687->2689 2691 age <= 43.5 entropy = 0.893 samples = 42 value = [29, 13] 2690->2691 2730 entropy = 0.0 samples = 2 value = [0, 2] 2690->2730 2692 age <= 41.5 entropy = 0.702 samples = 21 value = [17, 4] 2691->2692 2709 age <= 44.5 entropy = 0.985 samples = 21 value = [12, 9] 2691->2709 2693 race_Asian <= 0.5 entropy = 0.811 samples = 16 value = [12, 4] 2692->2693 2708 entropy = 0.0 samples = 5 value = [5, 0] 2692->2708 2694 hours-per-week <= 71.0 entropy = 0.722 samples = 15 value = [12, 3] 2693->2694 2707 entropy = 0.0 samples = 1 value = [0, 1] 2693->2707 2695 hours-per-week <= 67.5 entropy = 0.811 samples = 12 value = [9, 3] 2694->2695 2706 entropy = 0.0 samples = 3 value = [3, 0] 2694->2706 2696 age <= 39.5 entropy = 0.722 samples = 10 value = [8, 2] 2695->2696 2705 entropy = 1.0 samples = 2 value = [1, 1] 2695->2705 2697 education <= 10.5 entropy = 0.863 samples = 7 value = [5, 2] 2696->2697 2704 entropy = 0.0 samples = 3 value = [3, 0] 2696->2704 2698 age <= 36.5 entropy = 0.971 samples = 5 value = [3, 2] 2697->2698 2703 entropy = 0.0 samples = 2 value = [2, 0] 2697->2703 2699 entropy = 0.0 samples = 1 value = [0, 1] 2698->2699 2700 workclass_Public <= 0.5 entropy = 0.811 samples = 4 value = [3, 1] 2698->2700 2701 entropy = 0.0 samples = 2 value = [2, 0] 2700->2701 2702 entropy = 1.0 samples = 2 value = [1, 1] 2700->2702 2710 entropy = 0.0 samples = 2 value = [0, 2] 2709->2710 2711 age <= 56.5 entropy = 0.949 samples = 19 value = [12, 7] 2709->2711 2712 age <= 50.5 entropy = 0.918 samples = 18 value = [12, 6] 2711->2712 2729 entropy = 0.0 samples = 1 value = [0, 1] 2711->2729 2713 age <= 45.5 entropy = 0.985 samples = 14 value = [8, 6] 2712->2713 2728 entropy = 0.0 samples = 4 value = [4, 0] 2712->2728 2714 entropy = 0.0 samples = 2 value = [2, 0] 2713->2714 2715 education <= 10.5 entropy = 1.0 samples = 12 value = [6, 6] 2713->2715 2716 age <= 48.5 entropy = 0.954 samples = 8 value = [5, 3] 2715->2716 2725 age <= 47.5 entropy = 0.811 samples = 4 value = [1, 3] 2715->2725 2717 hours-per-week <= 65.0 entropy = 1.0 samples = 4 value = [2, 2] 2716->2717 2722 age <= 49.5 entropy = 0.811 samples = 4 value = [3, 1] 2716->2722 2718 age <= 47.0 entropy = 0.918 samples = 3 value = [2, 1] 2717->2718 2721 entropy = 0.0 samples = 1 value = [0, 1] 2717->2721 2719 entropy = 1.0 samples = 2 value = [1, 1] 2718->2719 2720 entropy = 0.0 samples = 1 value = [1, 0] 2718->2720 2723 entropy = 0.0 samples = 2 value = [2, 0] 2722->2723 2724 entropy = 1.0 samples = 2 value = [1, 1] 2722->2724 2726 entropy = 1.0 samples = 2 value = [1, 1] 2725->2726 2727 entropy = 0.0 samples = 2 value = [0, 2] 2725->2727 2732 entropy = 0.0 samples = 1 value = [1, 0] 2731->2732 2733 entropy = 0.0 samples = 6 value = [0, 6] 2731->2733 2735 age <= 59.5 entropy = 0.974 samples = 153 value = [62, 91] 2734->2735 2876 entropy = 0.0 samples = 3 value = [0, 3] 2734->2876 2736 hours-per-week <= 59.0 entropy = 0.969 samples = 151 value = [60, 91] 2735->2736 2875 entropy = 0.0 samples = 2 value = [2, 0] 2735->2875 2737 hours-per-week <= 53.0 entropy = 0.986 samples = 128 value = [55, 73] 2736->2737 2858 hours-per-week <= 91.5 entropy = 0.755 samples = 23 value = [5, 18] 2736->2858 2738 hours-per-week <= 45.5 entropy = 0.953 samples = 110 value = [41, 69] 2737->2738 2843 age <= 52.5 entropy = 0.764 samples = 18 value = [14, 4] 2737->2843 2739 hours-per-week <= 44.5 entropy = 0.994 samples = 44 value = [20, 24] 2738->2739 2780 education <= 10.5 entropy = 0.902 samples = 66 value = [21, 45] 2738->2780 2740 age <= 39.725 entropy = 0.764 samples = 9 value = [2, 7] 2739->2740 2747 age <= 36.5 entropy = 0.999 samples = 35 value = [18, 17] 2739->2747 2741 age <= 36.5 entropy = 0.918 samples = 6 value = [2, 4] 2740->2741 2746 entropy = 0.0 samples = 3 value = [0, 3] 2740->2746 2742 entropy = 1.0 samples = 2 value = [1, 1] 2741->2742 2743 age <= 38.225 entropy = 0.811 samples = 4 value = [1, 3] 2741->2743 2744 entropy = 0.0 samples = 2 value = [0, 2] 2743->2744 2745 entropy = 1.0 samples = 2 value = [1, 1] 2743->2745 2748 entropy = 0.0 samples = 2 value = [2, 0] 2747->2748 2749 sex_Male <= 0.5 entropy = 0.999 samples = 33 value = [16, 17] 2747->2749 2750 age <= 41.0 entropy = 0.811 samples = 4 value = [3, 1] 2749->2750 2753 age <= 54.5 entropy = 0.992 samples = 29 value = [13, 16] 2749->2753 2751 entropy = 0.0 samples = 1 value = [0, 1] 2750->2751 2752 entropy = 0.0 samples = 3 value = [3, 0] 2750->2752 2754 age <= 50.5 entropy = 0.966 samples = 23 value = [9, 14] 2753->2754 2773 age <= 55.5 entropy = 0.918 samples = 6 value = [4, 2] 2753->2773 2755 age <= 46.5 entropy = 1.0 samples = 18 value = [9, 9] 2754->2755 2772 entropy = 0.0 samples = 5 value = [0, 5] 2754->2772 2756 age <= 45.0 entropy = 0.971 samples = 10 value = [4, 6] 2755->2756 2767 age <= 48.0 entropy = 0.954 samples = 8 value = [5, 3] 2755->2767 2757 education <= 10.5 entropy = 1.0 samples = 8 value = [4, 4] 2756->2757 2766 entropy = 0.0 samples = 2 value = [0, 2] 2756->2766 2758 age <= 39.5 entropy = 0.985 samples = 7 value = [4, 3] 2757->2758 2765 entropy = 0.0 samples = 1 value = [0, 1] 2757->2765 2759 age <= 38.0 entropy = 0.811 samples = 4 value = [3, 1] 2758->2759 2762 age <= 43.0 entropy = 0.918 samples = 3 value = [1, 2] 2758->2762 2760 entropy = 0.918 samples = 3 value = [2, 1] 2759->2760 2761 entropy = 0.0 samples = 1 value = [1, 0] 2759->2761 2763 entropy = 0.0 samples = 2 value = [0, 2] 2762->2763 2764 entropy = 0.0 samples = 1 value = [1, 0] 2762->2764 2768 entropy = 0.0 samples = 3 value = [3, 0] 2767->2768 2769 education <= 10.5 entropy = 0.971 samples = 5 value = [2, 3] 2767->2769 2770 entropy = 0.0 samples = 3 value = [0, 3] 2769->2770 2771 entropy = 0.0 samples = 2 value = [2, 0] 2769->2771 2774 entropy = 0.0 samples = 1 value = [1, 0] 2773->2774 2775 education <= 10.5 entropy = 0.971 samples = 5 value = [3, 2] 2773->2775 2776 age <= 57.5 entropy = 0.811 samples = 4 value = [3, 1] 2775->2776 2779 entropy = 0.0 samples = 1 value = [0, 1] 2775->2779 2777 entropy = 0.0 samples = 2 value = [2, 0] 2776->2777 2778 entropy = 1.0 samples = 2 value = [1, 1] 2776->2778 2781 sex_Female <= 0.5 entropy = 0.94 samples = 56 value = [20, 36] 2780->2781 2840 age <= 52.5 entropy = 0.469 samples = 10 value = [1, 9] 2780->2840 2782 age <= 45.0 entropy = 0.951 samples = 54 value = [20, 34] 2781->2782 2839 entropy = 0.0 samples = 2 value = [0, 2] 2781->2839 2783 age <= 43.5 entropy = 0.985 samples = 35 value = [15, 20] 2782->2783 2822 hours-per-week <= 49.0 entropy = 0.831 samples = 19 value = [5, 14] 2782->2822 2784 race_Amer-Indian <= 0.5 entropy = 0.977 samples = 34 value = [14, 20] 2783->2784 2821 entropy = 0.0 samples = 1 value = [1, 0] 2783->2821 2785 hours-per-week <= 47.0 entropy = 0.983 samples = 33 value = [14, 19] 2784->2785 2820 entropy = 0.0 samples = 1 value = [0, 1] 2784->2820 2786 entropy = 0.0 samples = 1 value = [0, 1] 2785->2786 2787 race_White <= 0.5 entropy = 0.989 samples = 32 value = [14, 18] 2785->2787 2788 hours-per-week <= 51.0 entropy = 0.918 samples = 3 value = [2, 1] 2787->2788 2791 age <= 41.5 entropy = 0.978 samples = 29 value = [12, 17] 2787->2791 2789 entropy = 0.0 samples = 2 value = [2, 0] 2788->2789 2790 entropy = 0.0 samples = 1 value = [0, 1] 2788->2790 2792 hours-per-week <= 51.0 entropy = 0.946 samples = 22 value = [8, 14] 2791->2792 2813 hours-per-week <= 51.0 entropy = 0.985 samples = 7 value = [4, 3] 2791->2813 2793 age <= 36.5 entropy = 0.852 samples = 18 value = [5, 13] 2792->2793 2810 age <= 38.725 entropy = 0.811 samples = 4 value = [3, 1] 2792->2810 2794 entropy = 0.0 samples = 2 value = [0, 2] 2793->2794 2795 age <= 38.725 entropy = 0.896 samples = 16 value = [5, 11] 2793->2795 2796 age <= 37.5 entropy = 0.722 samples = 5 value = [1, 4] 2795->2796 2801 age <= 39.5 entropy = 0.946 samples = 11 value = [4, 7] 2795->2801 2797 hours-per-week <= 49.0 entropy = 0.918 samples = 3 value = [1, 2] 2796->2797 2800 entropy = 0.0 samples = 2 value = [0, 2] 2796->2800 2798 entropy = 0.0 samples = 1 value = [0, 1] 2797->2798 2799 entropy = 1.0 samples = 2 value = [1, 1] 2797->2799 2802 hours-per-week <= 49.0 entropy = 1.0 samples = 4 value = [2, 2] 2801->2802 2805 hours-per-week <= 49.0 entropy = 0.863 samples = 7 value = [2, 5] 2801->2805 2803 entropy = 0.0 samples = 1 value = [1, 0] 2802->2803 2804 entropy = 0.918 samples = 3 value = [1, 2] 2802->2804 2806 entropy = 0.0 samples = 1 value = [0, 1] 2805->2806 2807 age <= 40.5 entropy = 0.918 samples = 6 value = [2, 4] 2805->2807 2808 entropy = 0.918 samples = 3 value = [1, 2] 2807->2808 2809 entropy = 0.918 samples = 3 value = [1, 2] 2807->2809 2811 entropy = 0.0 samples = 2 value = [2, 0] 2810->2811 2812 entropy = 1.0 samples = 2 value = [1, 1] 2810->2812 2814 age <= 42.5 entropy = 0.918 samples = 6 value = [4, 2] 2813->2814 2819 entropy = 0.0 samples = 1 value = [0, 1] 2813->2819 2815 entropy = 0.0 samples = 2 value = [2, 0] 2814->2815 2816 hours-per-week <= 49.0 entropy = 1.0 samples = 4 value = [2, 2] 2814->2816 2817 entropy = 0.0 samples = 1 value = [1, 0] 2816->2817 2818 entropy = 0.918 samples = 3 value = [1, 2] 2816->2818 2823 entropy = 0.0 samples = 1 value = [0, 1] 2822->2823 2824 age <= 58.0 entropy = 0.852 samples = 18 value = [5, 13] 2822->2824 2825 age <= 55.0 entropy = 0.811 samples = 16 value = [4, 12] 2824->2825 2838 entropy = 1.0 samples = 2 value = [1, 1] 2824->2838 2826 age <= 53.5 entropy = 0.863 samples = 14 value = [4, 10] 2825->2826 2837 entropy = 0.0 samples = 2 value = [0, 2] 2825->2837 2827 age <= 49.5 entropy = 0.779 samples = 13 value = [3, 10] 2826->2827 2836 entropy = 0.0 samples = 1 value = [1, 0] 2826->2836 2828 age <= 48.5 entropy = 0.918 samples = 9 value = [3, 6] 2827->2828 2835 entropy = 0.0 samples = 4 value = [0, 4] 2827->2835 2829 age <= 47.5 entropy = 0.863 samples = 7 value = [2, 5] 2828->2829 2834 entropy = 1.0 samples = 2 value = [1, 1] 2828->2834 2830 age <= 46.5 entropy = 0.918 samples = 6 value = [2, 4] 2829->2830 2833 entropy = 0.0 samples = 1 value = [0, 1] 2829->2833 2831 entropy = 0.918 samples = 3 value = [1, 2] 2830->2831 2832 entropy = 0.918 samples = 3 value = [1, 2] 2830->2832 2841 entropy = 0.0 samples = 9 value = [0, 9] 2840->2841 2842 entropy = 0.0 samples = 1 value = [1, 0] 2840->2842 2844 age <= 48.0 entropy = 0.811 samples = 16 value = [12, 4] 2843->2844 2857 entropy = 0.0 samples = 2 value = [2, 0] 2843->2857 2845 age <= 41.5 entropy = 0.65 samples = 12 value = [10, 2] 2844->2845 2852 hours-per-week <= 55.5 entropy = 1.0 samples = 4 value = [2, 2] 2844->2852 2846 hours-per-week <= 55.5 entropy = 0.811 samples = 8 value = [6, 2] 2845->2846 2851 entropy = 0.0 samples = 4 value = [4, 0] 2845->2851 2847 age <= 38.225 entropy = 0.592 samples = 7 value = [6, 1] 2846->2847 2850 entropy = 0.0 samples = 1 value = [0, 1] 2846->2850 2848 entropy = 1.0 samples = 2 value = [1, 1] 2847->2848 2849 entropy = 0.0 samples = 5 value = [5, 0] 2847->2849 2853 education <= 10.5 entropy = 0.918 samples = 3 value = [1, 2] 2852->2853 2856 entropy = 0.0 samples = 1 value = [1, 0] 2852->2856 2854 entropy = 1.0 samples = 2 value = [1, 1] 2853->2854 2855 entropy = 0.0 samples = 1 value = [0, 1] 2853->2855 2859 sex_Male <= 0.5 entropy = 0.684 samples = 22 value = [4, 18] 2858->2859 2874 entropy = 0.0 samples = 1 value = [1, 0] 2858->2874 2860 entropy = 0.0 samples = 1 value = [1, 0] 2859->2860 2861 hours-per-week <= 67.5 entropy = 0.592 samples = 21 value = [3, 18] 2859->2861 2862 race_Black <= 0.5 entropy = 0.722 samples = 15 value = [3, 12] 2861->2862 2873 entropy = 0.0 samples = 6 value = [0, 6] 2861->2873 2863 age <= 51.0 entropy = 0.592 samples = 14 value = [2, 12] 2862->2863 2872 entropy = 0.0 samples = 1 value = [1, 0] 2862->2872 2864 age <= 47.5 entropy = 0.684 samples = 11 value = [2, 9] 2863->2864 2871 entropy = 0.0 samples = 3 value = [0, 3] 2863->2871 2865 hours-per-week <= 62.5 entropy = 0.503 samples = 9 value = [1, 8] 2864->2865 2870 entropy = 1.0 samples = 2 value = [1, 1] 2864->2870 2866 entropy = 0.0 samples = 6 value = [0, 6] 2865->2866 2867 age <= 41.725 entropy = 0.918 samples = 3 value = [1, 2] 2865->2867 2868 entropy = 1.0 samples = 2 value = [1, 1] 2867->2868 2869 entropy = 0.0 samples = 1 value = [0, 1] 2867->2869 2878 sex_Female <= 0.5 entropy = 0.837 samples = 90 value = [66, 24] 2877->2878 2945 entropy = 0.0 samples = 11 value = [11, 0] 2877->2945 2879 age <= 76.0 entropy = 0.903 samples = 69 value = [47, 22] 2878->2879 2936 education <= 9.5 entropy = 0.454 samples = 21 value = [19, 2] 2878->2936 2880 hours-per-week <= 36.0 entropy = 0.923 samples = 65 value = [43, 22] 2879->2880 2935 entropy = 0.0 samples = 4 value = [4, 0] 2879->2935 2881 education <= 9.5 entropy = 0.811 samples = 4 value = [1, 3] 2880->2881 2884 age <= 65.5 entropy = 0.895 samples = 61 value = [42, 19] 2880->2884 2882 entropy = 0.0 samples = 1 value = [1, 0] 2881->2882 2883 entropy = 0.0 samples = 3 value = [0, 3] 2881->2883 2885 hours-per-week <= 62.5 entropy = 0.967 samples = 33 value = [20, 13] 2884->2885 2912 hours-per-week <= 46.5 entropy = 0.75 samples = 28 value = [22, 6] 2884->2912 2886 workclass_Self-emp <= 0.5 entropy = 0.981 samples = 31 value = [18, 13] 2885->2886 2911 entropy = 0.0 samples = 2 value = [2, 0] 2885->2911 2887 race_Black <= 0.5 entropy = 0.902 samples = 22 value = [15, 7] 2886->2887 2902 hours-per-week <= 52.0 entropy = 0.918 samples = 9 value = [3, 6] 2886->2902 2888 education <= 9.5 entropy = 0.863 samples = 21 value = [15, 6] 2887->2888 2901 entropy = 0.0 samples = 1 value = [0, 1] 2887->2901 2889 hours-per-week <= 54.0 entropy = 0.971 samples = 15 value = [9, 6] 2888->2889 2900 entropy = 0.0 samples = 6 value = [6, 0] 2888->2900 2890 hours-per-week <= 42.5 entropy = 0.94 samples = 14 value = [9, 5] 2889->2890 2899 entropy = 0.0 samples = 1 value = [0, 1] 2889->2899 2891 race_Asian <= 0.5 entropy = 0.994 samples = 11 value = [6, 5] 2890->2891 2898 entropy = 0.0 samples = 3 value = [3, 0] 2890->2898 2892 age <= 64.5 entropy = 1.0 samples = 10 value = [5, 5] 2891->2892 2897 entropy = 0.0 samples = 1 value = [1, 0] 2891->2897 2893 age <= 63.5 entropy = 0.985 samples = 7 value = [4, 3] 2892->2893 2896 entropy = 0.918 samples = 3 value = [1, 2] 2892->2896 2894 entropy = 1.0 samples = 4 value = [2, 2] 2893->2894 2895 entropy = 0.918 samples = 3 value = [2, 1] 2893->2895 2903 education <= 10.5 entropy = 0.954 samples = 8 value = [3, 5] 2902->2903 2910 entropy = 0.0 samples = 1 value = [0, 1] 2902->2910 2904 age <= 64.0 entropy = 0.918 samples = 6 value = [2, 4] 2903->2904 2909 entropy = 1.0 samples = 2 value = [1, 1] 2903->2909 2905 hours-per-week <= 42.5 entropy = 0.811 samples = 4 value = [1, 3] 2904->2905 2908 entropy = 1.0 samples = 2 value = [1, 1] 2904->2908 2906 entropy = 0.0 samples = 2 value = [0, 2] 2905->2906 2907 entropy = 1.0 samples = 2 value = [1, 1] 2905->2907 2913 race_Asian <= 0.5 entropy = 0.845 samples = 22 value = [16, 6] 2912->2913 2934 entropy = 0.0 samples = 6 value = [6, 0] 2912->2934 2914 workclass_Self-emp <= 0.5 entropy = 0.792 samples = 21 value = [16, 5] 2913->2914 2933 entropy = 0.0 samples = 1 value = [0, 1] 2913->2933 2915 age <= 73.5 entropy = 0.94 samples = 14 value = [9, 5] 2914->2915 2932 entropy = 0.0 samples = 7 value = [7, 0] 2914->2932 2916 hours-per-week <= 42.5 entropy = 0.89 samples = 13 value = [9, 4] 2915->2916 2931 entropy = 0.0 samples = 1 value = [0, 1] 2915->2931 2917 age <= 71.0 entropy = 0.811 samples = 12 value = [9, 3] 2916->2917 2930 entropy = 0.0 samples = 1 value = [0, 1] 2916->2930 2918 hours-per-week <= 38.5 entropy = 0.881 samples = 10 value = [7, 3] 2917->2918 2929 entropy = 0.0 samples = 2 value = [2, 0] 2917->2929 2919 entropy = 0.0 samples = 1 value = [1, 0] 2918->2919 2920 age <= 69.5 entropy = 0.918 samples = 9 value = [6, 3] 2918->2920 2921 age <= 67.5 entropy = 0.811 samples = 8 value = [6, 2] 2920->2921 2928 entropy = 0.0 samples = 1 value = [0, 1] 2920->2928 2922 age <= 66.5 entropy = 0.971 samples = 5 value = [3, 2] 2921->2922 2927 entropy = 0.0 samples = 3 value = [3, 0] 2921->2927 2923 education <= 9.5 entropy = 0.918 samples = 3 value = [2, 1] 2922->2923 2926 entropy = 1.0 samples = 2 value = [1, 1] 2922->2926 2924 entropy = 0.0 samples = 1 value = [1, 0] 2923->2924 2925 entropy = 1.0 samples = 2 value = [1, 1] 2923->2925 2937 workclass_Self-emp <= 0.5 entropy = 0.619 samples = 13 value = [11, 2] 2936->2937 2944 entropy = 0.0 samples = 8 value = [8, 0] 2936->2944 2938 age <= 73.5 entropy = 0.439 samples = 11 value = [10, 1] 2937->2938 2943 entropy = 1.0 samples = 2 value = [1, 1] 2937->2943 2939 entropy = 0.0 samples = 8 value = [8, 0] 2938->2939 2940 age <= 74.5 entropy = 0.918 samples = 3 value = [2, 1] 2938->2940 2941 entropy = 0.0 samples = 1 value = [0, 1] 2940->2941 2942 entropy = 0.0 samples = 2 value = [2, 0] 2940->2942 2947 age <= 28.5 entropy = 0.974 samples = 794 value = [322, 472] 2946->2947 3526 age <= 33.5 entropy = 0.781 samples = 721 value = [167, 554] 2946->3526 2948 age <= 24.5 entropy = 0.784 samples = 60 value = [46, 14] 2947->2948 2983 hours-per-week <= 31.0 entropy = 0.955 samples = 734 value = [276, 458] 2947->2983 2949 entropy = 0.0 samples = 11 value = [11, 0] 2948->2949 2950 sex_Female <= 0.5 entropy = 0.863 samples = 49 value = [35, 14] 2948->2950 2951 education <= 12.5 entropy = 0.722 samples = 35 value = [28, 7] 2950->2951 2972 race_White <= 0.5 entropy = 1.0 samples = 14 value = [7, 7] 2950->2972 2952 entropy = 0.0 samples = 7 value = [7, 0] 2951->2952 2953 hours-per-week <= 34.0 entropy = 0.811 samples = 28 value = [21, 7] 2951->2953 2954 entropy = 0.0 samples = 3 value = [3, 0] 2953->2954 2955 education <= 13.5 entropy = 0.855 samples = 25 value = [18, 7] 2953->2955 2956 hours-per-week <= 40.5 entropy = 0.902 samples = 22 value = [15, 7] 2955->2956 2971 entropy = 0.0 samples = 3 value = [3, 0] 2955->2971 2957 workclass_Public <= 0.5 entropy = 0.918 samples = 21 value = [14, 7] 2956->2957 2970 entropy = 0.0 samples = 1 value = [1, 0] 2956->2970 2958 age <= 25.5 entropy = 0.852 samples = 18 value = [13, 5] 2957->2958 2967 age <= 25.5 entropy = 0.918 samples = 3 value = [1, 2] 2957->2967 2959 entropy = 0.0 samples = 2 value = [2, 0] 2958->2959 2960 age <= 26.5 entropy = 0.896 samples = 16 value = [11, 5] 2958->2960 2961 entropy = 0.918 samples = 3 value = [1, 2] 2960->2961 2962 race_Black <= 0.5 entropy = 0.779 samples = 13 value = [10, 3] 2960->2962 2963 age <= 27.5 entropy = 0.811 samples = 12 value = [9, 3] 2962->2963 2966 entropy = 0.0 samples = 1 value = [1, 0] 2962->2966 2964 entropy = 0.722 samples = 5 value = [4, 1] 2963->2964 2965 entropy = 0.863 samples = 7 value = [5, 2] 2963->2965 2968 entropy = 0.0 samples = 1 value = [0, 1] 2967->2968 2969 entropy = 1.0 samples = 2 value = [1, 1] 2967->2969 2973 entropy = 0.0 samples = 2 value = [2, 0] 2972->2973 2974 age <= 26.5 entropy = 0.98 samples = 12 value = [5, 7] 2972->2974 2975 hours-per-week <= 35.0 entropy = 0.863 samples = 7 value = [5, 2] 2974->2975 2982 entropy = 0.0 samples = 5 value = [0, 5] 2974->2982 2976 entropy = 0.0 samples = 1 value = [0, 1] 2975->2976 2977 age <= 25.5 entropy = 0.65 samples = 6 value = [5, 1] 2975->2977 2978 entropy = 0.0 samples = 3 value = [3, 0] 2977->2978 2979 education <= 13.5 entropy = 0.918 samples = 3 value = [2, 1] 2977->2979 2980 entropy = 0.0 samples = 1 value = [0, 1] 2979->2980 2981 entropy = 0.0 samples = 2 value = [2, 0] 2979->2981 2984 sex_Female <= 0.5 entropy = 0.97 samples = 108 value = [65, 43] 2983->2984 3071 age <= 36.5 entropy = 0.922 samples = 626 value = [211, 415] 2983->3071 2985 education <= 14.5 entropy = 0.913 samples = 70 value = [47, 23] 2984->2985 3034 age <= 67.5 entropy = 0.998 samples = 38 value = [18, 20] 2984->3034 2986 age <= 42.5 entropy = 0.811 samples = 56 value = [42, 14] 2985->2986 3025 age <= 55.5 entropy = 0.94 samples = 14 value = [5, 9] 2985->3025 2987 hours-per-week <= 24.5 entropy = 0.323 samples = 17 value = [16, 1] 2986->2987 2994 education <= 12.5 entropy = 0.918 samples = 39 value = [26, 13] 2986->2994 2988 entropy = 0.0 samples = 9 value = [9, 0] 2987->2988 2989 hours-per-week <= 27.5 entropy = 0.544 samples = 8 value = [7, 1] 2987->2989 2990 age <= 31.0 entropy = 0.918 samples = 3 value = [2, 1] 2989->2990 2993 entropy = 0.0 samples = 5 value = [5, 0] 2989->2993 2991 entropy = 0.0 samples = 1 value = [1, 0] 2990->2991 2992 entropy = 1.0 samples = 2 value = [1, 1] 2990->2992 2995 entropy = 0.0 samples = 4 value = [4, 0] 2994->2995 2996 workclass_Self-emp <= 0.5 entropy = 0.952 samples = 35 value = [22, 13] 2994->2996 2997 hours-per-week <= 28.0 entropy = 0.998 samples = 17 value = [8, 9] 2996->2997 3010 age <= 62.0 entropy = 0.764 samples = 18 value = [14, 4] 2996->3010 2998 hours-per-week <= 10.5 entropy = 0.94 samples = 14 value = [5, 9] 2997->2998 3009 entropy = 0.0 samples = 3 value = [3, 0] 2997->3009 2999 entropy = 0.0 samples = 3 value = [0, 3] 2998->2999 3000 workclass_Public <= 0.5 entropy = 0.994 samples = 11 value = [5, 6] 2998->3000 3001 hours-per-week <= 13.5 entropy = 0.918 samples = 9 value = [3, 6] 3000->3001 3008 entropy = 0.0 samples = 2 value = [2, 0] 3000->3008 3002 entropy = 0.0 samples = 1 value = [1, 0] 3001->3002 3003 hours-per-week <= 22.0 entropy = 0.811 samples = 8 value = [2, 6] 3001->3003 3004 entropy = 0.0 samples = 5 value = [0, 5] 3003->3004 3005 age <= 50.0 entropy = 0.918 samples = 3 value = [2, 1] 3003->3005 3006 entropy = 0.0 samples = 1 value = [0, 1] 3005->3006 3007 entropy = 0.0 samples = 2 value = [2, 0] 3005->3007 3011 entropy = 0.0 samples = 8 value = [8, 0] 3010->3011 3012 age <= 63.5 entropy = 0.971 samples = 10 value = [6, 4] 3010->3012 3013 entropy = 0.0 samples = 1 value = [0, 1] 3012->3013 3014 age <= 66.0 entropy = 0.918 samples = 9 value = [6, 3] 3012->3014 3015 entropy = 0.0 samples = 2 value = [2, 0] 3014->3015 3016 hours-per-week <= 9.0 entropy = 0.985 samples = 7 value = [4, 3] 3014->3016 3017 entropy = 0.0 samples = 1 value = [0, 1] 3016->3017 3018 hours-per-week <= 11.0 entropy = 0.918 samples = 6 value = [4, 2] 3016->3018 3019 entropy = 0.0 samples = 2 value = [2, 0] 3018->3019 3020 education <= 13.5 entropy = 1.0 samples = 4 value = [2, 2] 3018->3020 3021 hours-per-week <= 16.0 entropy = 0.918 samples = 3 value = [2, 1] 3020->3021 3024 entropy = 0.0 samples = 1 value = [0, 1] 3020->3024 3022 entropy = 0.0 samples = 1 value = [0, 1] 3021->3022 3023 entropy = 0.0 samples = 2 value = [2, 0] 3021->3023 3026 entropy = 0.0 samples = 4 value = [0, 4] 3025->3026 3027 workclass_Self-emp <= 0.5 entropy = 1.0 samples = 10 value = [5, 5] 3025->3027 3028 age <= 57.0 entropy = 0.722 samples = 5 value = [1, 4] 3027->3028 3031 education <= 15.5 entropy = 0.722 samples = 5 value = [4, 1] 3027->3031 3029 entropy = 0.0 samples = 1 value = [1, 0] 3028->3029 3030 entropy = 0.0 samples = 4 value = [0, 4] 3028->3030 3032 entropy = 0.0 samples = 3 value = [3, 0] 3031->3032 3033 entropy = 1.0 samples = 2 value = [1, 1] 3031->3033 3035 education <= 14.5 entropy = 0.977 samples = 34 value = [14, 20] 3034->3035 3070 entropy = 0.0 samples = 4 value = [4, 0] 3034->3070 3036 education <= 13.5 entropy = 0.954 samples = 32 value = [12, 20] 3035->3036 3069 entropy = 0.0 samples = 2 value = [2, 0] 3035->3069 3037 hours-per-week <= 11.5 entropy = 0.985 samples = 28 value = [12, 16] 3036->3037 3068 entropy = 0.0 samples = 4 value = [0, 4] 3036->3068 3038 entropy = 0.0 samples = 2 value = [0, 2] 3037->3038 3039 age <= 51.5 entropy = 0.996 samples = 26 value = [12, 14] 3037->3039 3040 age <= 45.5 entropy = 0.976 samples = 22 value = [9, 13] 3039->3040 3065 age <= 60.5 entropy = 0.811 samples = 4 value = [3, 1] 3039->3065 3041 hours-per-week <= 24.5 entropy = 0.993 samples = 20 value = [9, 11] 3040->3041 3064 entropy = 0.0 samples = 2 value = [0, 2] 3040->3064 3042 hours-per-week <= 22.0 entropy = 0.954 samples = 8 value = [5, 3] 3041->3042 3051 age <= 42.0 entropy = 0.918 samples = 12 value = [4, 8] 3041->3051 3043 workclass_Private <= 0.5 entropy = 1.0 samples = 6 value = [3, 3] 3042->3043 3050 entropy = 0.0 samples = 2 value = [2, 0] 3042->3050 3044 hours-per-week <= 17.5 entropy = 0.811 samples = 4 value = [3, 1] 3043->3044 3049 entropy = 0.0 samples = 2 value = [0, 2] 3043->3049 3045 entropy = 0.0 samples = 1 value = [1, 0] 3044->3045 3046 workclass_Public <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 3044->3046 3047 entropy = 0.0 samples = 1 value = [1, 0] 3046->3047 3048 entropy = 1.0 samples = 2 value = [1, 1] 3046->3048 3052 race_Black <= 0.5 entropy = 0.845 samples = 11 value = [3, 8] 3051->3052 3063 entropy = 0.0 samples = 1 value = [1, 0] 3051->3063 3053 age <= 36.5 entropy = 0.722 samples = 10 value = [2, 8] 3052->3053 3062 entropy = 0.0 samples = 1 value = [1, 0] 3052->3062 3054 entropy = 0.0 samples = 4 value = [0, 4] 3053->3054 3055 education <= 12.5 entropy = 0.918 samples = 6 value = [2, 4] 3053->3055 3056 entropy = 0.0 samples = 1 value = [1, 0] 3055->3056 3057 age <= 38.0 entropy = 0.722 samples = 5 value = [1, 4] 3055->3057 3058 entropy = 0.0 samples = 2 value = [0, 2] 3057->3058 3059 age <= 40.0 entropy = 0.918 samples = 3 value = [1, 2] 3057->3059 3060 entropy = 0.0 samples = 1 value = [1, 0] 3059->3060 3061 entropy = 0.0 samples = 2 value = [0, 2] 3059->3061 3066 entropy = 0.0 samples = 2 value = [2, 0] 3065->3066 3067 entropy = 1.0 samples = 2 value = [1, 1] 3065->3067 3072 race_Hispanic <= 0.5 entropy = 0.988 samples = 163 value = [71, 92] 3071->3072 3189 race_Asian <= 0.5 entropy = 0.884 samples = 463 value = [140, 323] 3071->3189 3073 hours-per-week <= 34.0 entropy = 0.985 samples = 161 value = [69, 92] 3072->3073 3188 entropy = 0.0 samples = 2 value = [2, 0] 3072->3188 3074 entropy = 0.0 samples = 2 value = [0, 2] 3073->3074 3075 sex_Female <= 0.5 entropy = 0.987 samples = 159 value = [69, 90] 3073->3075 3076 hours-per-week <= 35.5 entropy = 0.994 samples = 132 value = [60, 72] 3075->3076 3163 age <= 30.5 entropy = 0.918 samples = 27 value = [9, 18] 3075->3163 3077 race_White <= 0.5 entropy = 0.722 samples = 5 value = [4, 1] 3076->3077 3080 workclass_Public <= 0.5 entropy = 0.99 samples = 127 value = [56, 71] 3076->3080 3078 entropy = 1.0 samples = 2 value = [1, 1] 3077->3078 3079 entropy = 0.0 samples = 3 value = [3, 0] 3077->3079 3081 hours-per-week <= 37.5 entropy = 0.981 samples = 105 value = [44, 61] 3080->3081 3142 age <= 33.5 entropy = 0.994 samples = 22 value = [12, 10] 3080->3142 3082 entropy = 0.0 samples = 2 value = [0, 2] 3081->3082 3083 age <= 34.5 entropy = 0.985 samples = 103 value = [44, 59] 3081->3083 3084 age <= 33.5 entropy = 0.969 samples = 78 value = [31, 47] 3083->3084 3123 education <= 15.0 entropy = 0.999 samples = 25 value = [13, 12] 3083->3123 3085 education <= 14.5 entropy = 0.987 samples = 67 value = [29, 38] 3084->3085 3120 education <= 13.5 entropy = 0.684 samples = 11 value = [2, 9] 3084->3120 3086 education <= 13.5 entropy = 0.976 samples = 61 value = [25, 36] 3085->3086 3115 race_Asian <= 0.5 entropy = 0.918 samples = 6 value = [4, 2] 3085->3115 3087 hours-per-week <= 39.0 entropy = 0.987 samples = 53 value = [23, 30] 3086->3087 3110 workclass_Private <= 0.5 entropy = 0.811 samples = 8 value = [2, 6] 3086->3110 3088 entropy = 0.0 samples = 1 value = [0, 1] 3087->3088 3089 race_Asian <= 0.5 entropy = 0.99 samples = 52 value = [23, 29] 3087->3089 3090 age <= 30.5 entropy = 0.981 samples = 50 value = [21, 29] 3089->3090 3109 entropy = 0.0 samples = 2 value = [2, 0] 3089->3109 3091 age <= 29.5 entropy = 0.997 samples = 15 value = [7, 8] 3090->3091 3094 age <= 31.5 entropy = 0.971 samples = 35 value = [14, 21] 3090->3094 3092 entropy = 0.954 samples = 8 value = [3, 5] 3091->3092 3093 entropy = 0.985 samples = 7 value = [4, 3] 3091->3093 3095 education <= 12.5 entropy = 0.881 samples = 10 value = [3, 7] 3094->3095 3098 education <= 12.5 entropy = 0.99 samples = 25 value = [11, 14] 3094->3098 3096 entropy = 0.0 samples = 2 value = [0, 2] 3095->3096 3097 entropy = 0.954 samples = 8 value = [3, 5] 3095->3097 3099 entropy = 0.0 samples = 2 value = [2, 0] 3098->3099 3100 race_White <= 0.5 entropy = 0.966 samples = 23 value = [9, 14] 3098->3100 3101 entropy = 1.0 samples = 2 value = [1, 1] 3100->3101 3102 age <= 32.5 entropy = 0.959 samples = 21 value = [8, 13] 3100->3102 3103 workclass_Private <= 0.5 entropy = 0.994 samples = 11 value = [5, 6] 3102->3103 3106 workclass_Self-emp <= 0.5 entropy = 0.881 samples = 10 value = [3, 7] 3102->3106 3104 entropy = 0.0 samples = 1 value = [1, 0] 3103->3104 3105 entropy = 0.971 samples = 10 value = [4, 6] 3103->3105 3107 entropy = 0.954 samples = 8 value = [3, 5] 3106->3107 3108 entropy = 0.0 samples = 2 value = [0, 2] 3106->3108 3111 entropy = 0.0 samples = 1 value = [1, 0] 3110->3111 3112 hours-per-week <= 39.0 entropy = 0.592 samples = 7 value = [1, 6] 3110->3112 3113 entropy = 0.0 samples = 1 value = [1, 0] 3112->3113 3114 entropy = 0.0 samples = 6 value = [0, 6] 3112->3114 3116 workclass_Self-emp <= 0.5 entropy = 0.722 samples = 5 value = [4, 1] 3115->3116 3119 entropy = 0.0 samples = 1 value = [0, 1] 3115->3119 3117 entropy = 0.0 samples = 3 value = [3, 0] 3116->3117 3118 entropy = 1.0 samples = 2 value = [1, 1] 3116->3118 3121 entropy = 0.503 samples = 9 value = [1, 8] 3120->3121 3122 entropy = 1.0 samples = 2 value = [1, 1] 3120->3122 3124 hours-per-week <= 39.0 entropy = 0.988 samples = 23 value = [13, 10] 3123->3124 3141 entropy = 0.0 samples = 2 value = [0, 2] 3123->3141 3125 entropy = 0.0 samples = 1 value = [1, 0] 3124->3125 3126 workclass_Self-emp <= 0.5 entropy = 0.994 samples = 22 value = [12, 10] 3124->3126 3127 education <= 12.5 entropy = 0.982 samples = 19 value = [11, 8] 3126->3127 3138 education <= 13.0 entropy = 0.918 samples = 3 value = [1, 2] 3126->3138 3128 age <= 35.5 entropy = 0.918 samples = 3 value = [1, 2] 3127->3128 3131 age <= 35.5 entropy = 0.954 samples = 16 value = [10, 6] 3127->3131 3129 entropy = 0.0 samples = 2 value = [0, 2] 3128->3129 3130 entropy = 0.0 samples = 1 value = [1, 0] 3128->3130 3132 education <= 13.5 entropy = 0.811 samples = 8 value = [6, 2] 3131->3132 3135 education <= 13.5 entropy = 1.0 samples = 8 value = [4, 4] 3131->3135 3133 entropy = 0.0 samples = 4 value = [4, 0] 3132->3133 3134 entropy = 1.0 samples = 4 value = [2, 2] 3132->3134 3136 entropy = 1.0 samples = 6 value = [3, 3] 3135->3136 3137 entropy = 1.0 samples = 2 value = [1, 1] 3135->3137 3139 entropy = 1.0 samples = 2 value = [1, 1] 3138->3139 3140 entropy = 0.0 samples = 1 value = [0, 1] 3138->3140 3143 education <= 13.5 entropy = 0.918 samples = 15 value = [10, 5] 3142->3143 3158 age <= 34.5 entropy = 0.863 samples = 7 value = [2, 5] 3142->3158 3144 age <= 30.5 entropy = 0.98 samples = 12 value = [7, 5] 3143->3144 3157 entropy = 0.0 samples = 3 value = [3, 0] 3143->3157 3145 age <= 29.5 entropy = 0.811 samples = 4 value = [1, 3] 3144->3145 3150 education <= 12.5 entropy = 0.811 samples = 8 value = [6, 2] 3144->3150 3146 entropy = 0.0 samples = 1 value = [0, 1] 3145->3146 3147 race_Asian <= 0.5 entropy = 0.918 samples = 3 value = [1, 2] 3145->3147 3148 entropy = 1.0 samples = 2 value = [1, 1] 3147->3148 3149 entropy = 0.0 samples = 1 value = [0, 1] 3147->3149 3151 entropy = 0.0 samples = 2 value = [2, 0] 3150->3151 3152 race_White <= 0.5 entropy = 0.918 samples = 6 value = [4, 2] 3150->3152 3153 entropy = 0.0 samples = 2 value = [2, 0] 3152->3153 3154 age <= 31.5 entropy = 1.0 samples = 4 value = [2, 2] 3152->3154 3155 entropy = 1.0 samples = 2 value = [1, 1] 3154->3155 3156 entropy = 1.0 samples = 2 value = [1, 1] 3154->3156 3159 entropy = 0.0 samples = 4 value = [0, 4] 3158->3159 3160 age <= 35.5 entropy = 0.918 samples = 3 value = [2, 1] 3158->3160 3161 entropy = 1.0 samples = 2 value = [1, 1] 3160->3161 3162 entropy = 0.0 samples = 1 value = [1, 0] 3160->3162 3164 entropy = 0.0 samples = 5 value = [0, 5] 3163->3164 3165 age <= 35.5 entropy = 0.976 samples = 22 value = [9, 13] 3163->3165 3166 age <= 32.5 entropy = 0.998 samples = 19 value = [9, 10] 3165->3166 3187 entropy = 0.0 samples = 3 value = [0, 3] 3165->3187 3167 age <= 31.5 entropy = 0.811 samples = 8 value = [2, 6] 3166->3167 3174 hours-per-week <= 37.5 entropy = 0.946 samples = 11 value = [7, 4] 3166->3174 3168 education <= 12.5 entropy = 0.971 samples = 5 value = [2, 3] 3167->3168 3173 entropy = 0.0 samples = 3 value = [0, 3] 3167->3173 3169 entropy = 0.0 samples = 1 value = [1, 0] 3168->3169 3170 hours-per-week <= 37.5 entropy = 0.811 samples = 4 value = [1, 3] 3168->3170 3171 entropy = 0.0 samples = 2 value = [0, 2] 3170->3171 3172 entropy = 1.0 samples = 2 value = [1, 1] 3170->3172 3175 entropy = 0.0 samples = 1 value = [0, 1] 3174->3175 3176 age <= 34.5 entropy = 0.881 samples = 10 value = [7, 3] 3174->3176 3177 race_Black <= 0.5 entropy = 0.954 samples = 8 value = [5, 3] 3176->3177 3186 entropy = 0.0 samples = 2 value = [2, 0] 3176->3186 3178 workclass_Public <= 0.5 entropy = 0.863 samples = 7 value = [5, 2] 3177->3178 3185 entropy = 0.0 samples = 1 value = [0, 1] 3177->3185 3179 education <= 12.5 entropy = 0.971 samples = 5 value = [3, 2] 3178->3179 3184 entropy = 0.0 samples = 2 value = [2, 0] 3178->3184 3180 entropy = 0.0 samples = 1 value = [1, 0] 3179->3180 3181 age <= 33.5 entropy = 1.0 samples = 4 value = [2, 2] 3179->3181 3182 entropy = 1.0 samples = 2 value = [1, 1] 3181->3182 3183 entropy = 1.0 samples = 2 value = [1, 1] 3181->3183 3190 sex_Female <= 0.5 entropy = 0.86 samples = 431 value = [122, 309] 3189->3190 3495 sex_Male <= 0.5 entropy = 0.989 samples = 32 value = [18, 14] 3189->3495 3191 education <= 15.5 entropy = 0.83 samples = 370 value = [97, 273] 3190->3191 3434 age <= 53.0 entropy = 0.976 samples = 61 value = [25, 36] 3190->3434 3192 race_Amer-Indian <= 0.5 entropy = 0.846 samples = 355 value = [97, 258] 3191->3192 3433 entropy = 0.0 samples = 15 value = [0, 15] 3191->3433 3193 education <= 13.5 entropy = 0.84 samples = 353 value = [95, 258] 3192->3193 3432 entropy = 0.0 samples = 2 value = [2, 0] 3192->3432 3194 race_Hispanic <= 0.5 entropy = 0.886 samples = 237 value = [72, 165] 3193->3194 3355 age <= 82.0 entropy = 0.718 samples = 116 value = [23, 93] 3193->3355 3195 age <= 41.5 entropy = 0.882 samples = 236 value = [71, 165] 3194->3195 3354 entropy = 0.0 samples = 1 value = [1, 0] 3194->3354 3196 age <= 37.5 entropy = 0.96 samples = 60 value = [23, 37] 3195->3196 3231 workclass_Public <= 0.5 entropy = 0.845 samples = 176 value = [48, 128] 3195->3231 3197 education <= 12.5 entropy = 0.544 samples = 8 value = [1, 7] 3196->3197 3200 race_White <= 0.5 entropy = 0.983 samples = 52 value = [22, 30] 3196->3200 3198 entropy = 1.0 samples = 2 value = [1, 1] 3197->3198 3199 entropy = 0.0 samples = 6 value = [0, 6] 3197->3199 3201 entropy = 0.0 samples = 1 value = [1, 0] 3200->3201 3202 age <= 38.725 entropy = 0.977 samples = 51 value = [21, 30] 3200->3202 3203 workclass_Public <= 0.5 entropy = 0.9 samples = 19 value = [6, 13] 3202->3203 3212 hours-per-week <= 38.0 entropy = 0.997 samples = 32 value = [15, 17] 3202->3212 3204 hours-per-week <= 37.5 entropy = 0.852 samples = 18 value = [5, 13] 3203->3204 3211 entropy = 0.0 samples = 1 value = [1, 0] 3203->3211 3205 entropy = 0.0 samples = 1 value = [1, 0] 3204->3205 3206 workclass_Self-emp <= 0.5 entropy = 0.787 samples = 17 value = [4, 13] 3204->3206 3207 age <= 38.225 entropy = 0.837 samples = 15 value = [4, 11] 3206->3207 3210 entropy = 0.0 samples = 2 value = [0, 2] 3206->3210 3208 entropy = 0.811 samples = 8 value = [2, 6] 3207->3208 3209 entropy = 0.863 samples = 7 value = [2, 5] 3207->3209 3213 entropy = 0.0 samples = 2 value = [0, 2] 3212->3213 3214 workclass_Public <= 0.5 entropy = 1.0 samples = 30 value = [15, 15] 3212->3214 3215 education <= 12.5 entropy = 0.996 samples = 26 value = [14, 12] 3214->3215 3228 age <= 40.5 entropy = 0.811 samples = 4 value = [1, 3] 3214->3228 3216 entropy = 0.0 samples = 4 value = [4, 0] 3215->3216 3217 workclass_Self-emp <= 0.5 entropy = 0.994 samples = 22 value = [10, 12] 3215->3217 3218 age <= 40.5 entropy = 0.977 samples = 17 value = [7, 10] 3217->3218 3223 age <= 40.5 entropy = 0.971 samples = 5 value = [3, 2] 3217->3223 3219 age <= 39.5 entropy = 0.994 samples = 11 value = [5, 6] 3218->3219 3222 entropy = 0.918 samples = 6 value = [2, 4] 3218->3222 3220 entropy = 0.985 samples = 7 value = [3, 4] 3219->3220 3221 entropy = 1.0 samples = 4 value = [2, 2] 3219->3221 3224 age <= 39.5 entropy = 1.0 samples = 4 value = [2, 2] 3223->3224 3227 entropy = 0.0 samples = 1 value = [1, 0] 3223->3227 3225 entropy = 1.0 samples = 2 value = [1, 1] 3224->3225 3226 entropy = 1.0 samples = 2 value = [1, 1] 3224->3226 3229 entropy = 1.0 samples = 2 value = [1, 1] 3228->3229 3230 entropy = 0.0 samples = 2 value = [0, 2] 3228->3230 3232 age <= 51.5 entropy = 0.778 samples = 126 value = [29, 97] 3231->3232 3311 age <= 60.5 entropy = 0.958 samples = 50 value = [19, 31] 3231->3311 3233 age <= 49.5 entropy = 0.7 samples = 74 value = [14, 60] 3232->3233 3276 education <= 12.5 entropy = 0.867 samples = 52 value = [15, 37] 3232->3276 3234 race_White <= 0.5 entropy = 0.752 samples = 65 value = [14, 51] 3233->3234 3275 entropy = 0.0 samples = 9 value = [0, 9] 3233->3275 3235 entropy = 0.0 samples = 4 value = [0, 4] 3234->3235 3236 workclass_Private <= 0.5 entropy = 0.777 samples = 61 value = [14, 47] 3234->3236 3237 age <= 44.5 entropy = 0.961 samples = 13 value = [5, 8] 3236->3237 3248 age <= 44.5 entropy = 0.696 samples = 48 value = [9, 39] 3236->3248 3238 entropy = 0.0 samples = 2 value = [2, 0] 3237->3238 3239 education <= 12.5 entropy = 0.845 samples = 11 value = [3, 8] 3237->3239 3240 entropy = 0.0 samples = 1 value = [1, 0] 3239->3240 3241 age <= 45.5 entropy = 0.722 samples = 10 value = [2, 8] 3239->3241 3242 entropy = 0.0 samples = 3 value = [0, 3] 3241->3242 3243 age <= 46.5 entropy = 0.863 samples = 7 value = [2, 5] 3241->3243 3244 entropy = 0.918 samples = 3 value = [1, 2] 3243->3244 3245 age <= 47.5 entropy = 0.811 samples = 4 value = [1, 3] 3243->3245 3246 entropy = 0.0 samples = 1 value = [0, 1] 3245->3246 3247 entropy = 0.918 samples = 3 value = [1, 2] 3245->3247 3249 age <= 43.5 entropy = 0.426 samples = 23 value = [2, 21] 3248->3249 3256 hours-per-week <= 36.0 entropy = 0.855 samples = 25 value = [7, 18] 3248->3256 3250 education <= 12.5 entropy = 0.567 samples = 15 value = [2, 13] 3249->3250 3255 entropy = 0.0 samples = 8 value = [0, 8] 3249->3255 3251 entropy = 0.0 samples = 1 value = [0, 1] 3250->3251 3252 age <= 42.5 entropy = 0.592 samples = 14 value = [2, 12] 3250->3252 3253 entropy = 0.65 samples = 6 value = [1, 5] 3252->3253 3254 entropy = 0.544 samples = 8 value = [1, 7] 3252->3254 3257 entropy = 0.0 samples = 1 value = [0, 1] 3256->3257 3258 hours-per-week <= 38.5 entropy = 0.871 samples = 24 value = [7, 17] 3256->3258 3259 entropy = 0.0 samples = 1 value = [1, 0] 3258->3259 3260 age <= 46.5 entropy = 0.828 samples = 23 value = [6, 17] 3258->3260 3261 education <= 12.5 entropy = 0.75 samples = 14 value = [3, 11] 3260->3261 3268 education <= 12.5 entropy = 0.918 samples = 9 value = [3, 6] 3260->3268 3262 age <= 45.5 entropy = 0.971 samples = 5 value = [2, 3] 3261->3262 3265 age <= 45.5 entropy = 0.503 samples = 9 value = [1, 8] 3261->3265 3263 entropy = 0.0 samples = 1 value = [1, 0] 3262->3263 3264 entropy = 0.811 samples = 4 value = [1, 3] 3262->3264 3266 entropy = 0.0 samples = 4 value = [0, 4] 3265->3266 3267 entropy = 0.722 samples = 5 value = [1, 4] 3265->3267 3269 entropy = 0.0 samples = 1 value = [0, 1] 3268->3269 3270 age <= 48.5 entropy = 0.954 samples = 8 value = [3, 5] 3268->3270 3271 age <= 47.5 entropy = 1.0 samples = 4 value = [2, 2] 3270->3271 3274 entropy = 0.811 samples = 4 value = [1, 3] 3270->3274 3272 entropy = 1.0 samples = 2 value = [1, 1] 3271->3272 3273 entropy = 1.0 samples = 2 value = [1, 1] 3271->3273 3277 entropy = 0.0 samples = 5 value = [0, 5] 3276->3277 3278 age <= 65.0 entropy = 0.903 samples = 47 value = [15, 32] 3276->3278 3279 workclass_Self-emp <= 0.5 entropy = 0.926 samples = 44 value = [15, 29] 3278->3279 3310 entropy = 0.0 samples = 3 value = [0, 3] 3278->3310 3280 age <= 63.5 entropy = 0.974 samples = 32 value = [13, 19] 3279->3280 3305 age <= 53.5 entropy = 0.65 samples = 12 value = [2, 10] 3279->3305 3281 age <= 61.0 entropy = 0.987 samples = 30 value = [13, 17] 3280->3281 3304 entropy = 0.0 samples = 2 value = [0, 2] 3280->3304 3282 age <= 56.0 entropy = 0.954 samples = 24 value = [9, 15] 3281->3282 3299 hours-per-week <= 37.5 entropy = 0.918 samples = 6 value = [4, 2] 3281->3299 3283 hours-per-week <= 37.5 entropy = 1.0 samples = 12 value = [6, 6] 3282->3283 3294 age <= 57.5 entropy = 0.811 samples = 12 value = [3, 9] 3282->3294 3284 entropy = 0.0 samples = 1 value = [0, 1] 3283->3284 3285 age <= 53.5 entropy = 0.994 samples = 11 value = [6, 5] 3283->3285 3286 race_Black <= 0.5 entropy = 0.971 samples = 5 value = [2, 3] 3285->3286 3291 age <= 54.5 entropy = 0.918 samples = 6 value = [4, 2] 3285->3291 3287 entropy = 1.0 samples = 2 value = [1, 1] 3286->3287 3288 age <= 52.5 entropy = 0.918 samples = 3 value = [1, 2] 3286->3288 3289 entropy = 0.0 samples = 1 value = [0, 1] 3288->3289 3290 entropy = 1.0 samples = 2 value = [1, 1] 3288->3290 3292 entropy = 0.918 samples = 3 value = [2, 1] 3291->3292 3293 entropy = 0.918 samples = 3 value = [2, 1] 3291->3293 3295 entropy = 0.0 samples = 5 value = [0, 5] 3294->3295 3296 age <= 59.0 entropy = 0.985 samples = 7 value = [3, 4] 3294->3296 3297 entropy = 0.971 samples = 5 value = [2, 3] 3296->3297 3298 entropy = 1.0 samples = 2 value = [1, 1] 3296->3298 3300 entropy = 0.0 samples = 1 value = [1, 0] 3299->3300 3301 age <= 62.5 entropy = 0.971 samples = 5 value = [3, 2] 3299->3301 3302 entropy = 0.918 samples = 3 value = [2, 1] 3301->3302 3303 entropy = 1.0 samples = 2 value = [1, 1] 3301->3303 3306 entropy = 1.0 samples = 2 value = [1, 1] 3305->3306 3307 age <= 63.5 entropy = 0.469 samples = 10 value = [1, 9] 3305->3307 3308 entropy = 0.0 samples = 8 value = [0, 8] 3307->3308 3309 entropy = 1.0 samples = 2 value = [1, 1] 3307->3309 3312 age <= 56.5 entropy = 0.925 samples = 47 value = [16, 31] 3311->3312 3353 entropy = 0.0 samples = 3 value = [3, 0] 3311->3353 3313 education <= 12.5 entropy = 0.959 samples = 42 value = [16, 26] 3312->3313 3352 entropy = 0.0 samples = 5 value = [0, 5] 3312->3352 3314 age <= 49.0 entropy = 0.985 samples = 7 value = [4, 3] 3313->3314 3323 age <= 48.0 entropy = 0.928 samples = 35 value = [12, 23] 3313->3323 3315 race_Black <= 0.5 entropy = 0.971 samples = 5 value = [2, 3] 3314->3315 3322 entropy = 0.0 samples = 2 value = [2, 0] 3314->3322 3316 age <= 45.0 entropy = 1.0 samples = 4 value = [2, 2] 3315->3316 3321 entropy = 0.0 samples = 1 value = [0, 1] 3315->3321 3317 entropy = 0.0 samples = 1 value = [0, 1] 3316->3317 3318 age <= 46.5 entropy = 0.918 samples = 3 value = [2, 1] 3316->3318 3319 entropy = 0.0 samples = 1 value = [1, 0] 3318->3319 3320 entropy = 1.0 samples = 2 value = [1, 1] 3318->3320 3324 age <= 46.5 entropy = 0.998 samples = 17 value = [8, 9] 3323->3324 3339 age <= 51.5 entropy = 0.764 samples = 18 value = [4, 14] 3323->3339 3325 hours-per-week <= 36.0 entropy = 0.94 samples = 14 value = [5, 9] 3324->3325 3338 entropy = 0.0 samples = 3 value = [3, 0] 3324->3338 3326 entropy = 0.0 samples = 1 value = [0, 1] 3325->3326 3327 hours-per-week <= 38.5 entropy = 0.961 samples = 13 value = [5, 8] 3325->3327 3328 entropy = 0.0 samples = 1 value = [1, 0] 3327->3328 3329 age <= 45.5 entropy = 0.918 samples = 12 value = [4, 8] 3327->3329 3330 age <= 44.0 entropy = 0.991 samples = 9 value = [4, 5] 3329->3330 3337 entropy = 0.0 samples = 3 value = [0, 3] 3329->3337 3331 race_Black <= 0.5 entropy = 0.863 samples = 7 value = [2, 5] 3330->3331 3336 entropy = 0.0 samples = 2 value = [2, 0] 3330->3336 3332 age <= 42.5 entropy = 0.918 samples = 6 value = [2, 4] 3331->3332 3335 entropy = 0.0 samples = 1 value = [0, 1] 3331->3335 3333 entropy = 0.0 samples = 1 value = [0, 1] 3332->3333 3334 entropy = 0.971 samples = 5 value = [2, 3] 3332->3334 3340 entropy = 0.0 samples = 8 value = [0, 8] 3339->3340 3341 hours-per-week <= 36.5 entropy = 0.971 samples = 10 value = [4, 6] 3339->3341 3342 entropy = 0.0 samples = 1 value = [1, 0] 3341->3342 3343 age <= 54.5 entropy = 0.918 samples = 9 value = [3, 6] 3341->3343 3344 hours-per-week <= 39.0 entropy = 1.0 samples = 4 value = [2, 2] 3343->3344 3349 age <= 55.5 entropy = 0.722 samples = 5 value = [1, 4] 3343->3349 3345 entropy = 0.0 samples = 1 value = [1, 0] 3344->3345 3346 race_Black <= 0.5 entropy = 0.918 samples = 3 value = [1, 2] 3344->3346 3347 entropy = 1.0 samples = 2 value = [1, 1] 3346->3347 3348 entropy = 0.0 samples = 1 value = [0, 1] 3346->3348 3350 entropy = 0.0 samples = 2 value = [0, 2] 3349->3350 3351 entropy = 0.918 samples = 3 value = [1, 2] 3349->3351 3356 age <= 37.5 entropy = 0.704 samples = 115 value = [22, 93] 3355->3356 3431 entropy = 0.0 samples = 1 value = [1, 0] 3355->3431 3357 workclass_Private <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 3356->3357 3360 age <= 38.725 entropy = 0.677 samples = 112 value = [20, 92] 3356->3360 3358 entropy = 1.0 samples = 2 value = [1, 1] 3357->3358 3359 entropy = 0.0 samples = 1 value = [1, 0] 3357->3359 3361 entropy = 0.0 samples = 9 value = [0, 9] 3360->3361 3362 age <= 41.5 entropy = 0.71 samples = 103 value = [20, 83] 3360->3362 3363 workclass_Self-emp <= 0.5 entropy = 0.874 samples = 17 value = [5, 12] 3362->3363 3376 age <= 43.5 entropy = 0.668 samples = 86 value = [15, 71] 3362->3376 3364 education <= 14.5 entropy = 0.811 samples = 16 value = [4, 12] 3363->3364 3375 entropy = 0.0 samples = 1 value = [1, 0] 3363->3375 3365 age <= 40.5 entropy = 0.89 samples = 13 value = [4, 9] 3364->3365 3374 entropy = 0.0 samples = 3 value = [0, 3] 3364->3374 3366 workclass_Private <= 0.5 entropy = 0.764 samples = 9 value = [2, 7] 3365->3366 3371 workclass_Public <= 0.5 entropy = 1.0 samples = 4 value = [2, 2] 3365->3371 3367 entropy = 0.0 samples = 2 value = [0, 2] 3366->3367 3368 age <= 39.5 entropy = 0.863 samples = 7 value = [2, 5] 3366->3368 3369 entropy = 0.811 samples = 4 value = [1, 3] 3368->3369 3370 entropy = 0.918 samples = 3 value = [1, 2] 3368->3370 3372 entropy = 1.0 samples = 2 value = [1, 1] 3371->3372 3373 entropy = 1.0 samples = 2 value = [1, 1] 3371->3373 3377 entropy = 0.0 samples = 12 value = [0, 12] 3376->3377 3378 age <= 51.5 entropy = 0.727 samples = 74 value = [15, 59] 3376->3378 3379 education <= 14.5 entropy = 0.65 samples = 48 value = [8, 40] 3378->3379 3410 age <= 52.5 entropy = 0.84 samples = 26 value = [7, 19] 3378->3410 3380 age <= 49.5 entropy = 0.712 samples = 41 value = [8, 33] 3379->3380 3409 entropy = 0.0 samples = 7 value = [0, 7] 3379->3409 3381 race_Black <= 0.5 entropy = 0.764 samples = 36 value = [8, 28] 3380->3381 3408 entropy = 0.0 samples = 5 value = [0, 5] 3380->3408 3382 workclass_Private <= 0.5 entropy = 0.734 samples = 34 value = [7, 27] 3381->3382 3407 entropy = 1.0 samples = 2 value = [1, 1] 3381->3407 3383 age <= 47.5 entropy = 0.831 samples = 19 value = [5, 14] 3382->3383 3402 age <= 47.5 entropy = 0.567 samples = 15 value = [2, 13] 3382->3402 3384 hours-per-week <= 40.5 entropy = 0.94 samples = 14 value = [5, 9] 3383->3384 3401 entropy = 0.0 samples = 5 value = [0, 5] 3383->3401 3385 hours-per-week <= 36.5 entropy = 0.961 samples = 13 value = [5, 8] 3384->3385 3400 entropy = 0.0 samples = 1 value = [0, 1] 3384->3400 3386 entropy = 1.0 samples = 2 value = [1, 1] 3385->3386 3387 age <= 44.5 entropy = 0.946 samples = 11 value = [4, 7] 3385->3387 3388 workclass_Self-emp <= 0.5 entropy = 0.722 samples = 5 value = [1, 4] 3387->3388 3391 workclass_Self-emp <= 0.5 entropy = 1.0 samples = 6 value = [3, 3] 3387->3391 3389 entropy = 0.0 samples = 3 value = [0, 3] 3388->3389 3390 entropy = 1.0 samples = 2 value = [1, 1] 3388->3390 3392 hours-per-week <= 39.0 entropy = 0.971 samples = 5 value = [3, 2] 3391->3392 3399 entropy = 0.0 samples = 1 value = [0, 1] 3391->3399 3393 entropy = 0.0 samples = 1 value = [0, 1] 3392->3393 3394 age <= 45.5 entropy = 0.811 samples = 4 value = [3, 1] 3392->3394 3395 entropy = 0.0 samples = 1 value = [1, 0] 3394->3395 3396 age <= 46.5 entropy = 0.918 samples = 3 value = [2, 1] 3394->3396 3397 entropy = 1.0 samples = 2 value = [1, 1] 3396->3397 3398 entropy = 0.0 samples = 1 value = [1, 0] 3396->3398 3403 entropy = 0.0 samples = 10 value = [0, 10] 3402->3403 3404 age <= 48.5 entropy = 0.971 samples = 5 value = [2, 3] 3402->3404 3405 entropy = 0.918 samples = 3 value = [1, 2] 3404->3405 3406 entropy = 1.0 samples = 2 value = [1, 1] 3404->3406 3411 hours-per-week <= 37.5 entropy = 0.918 samples = 3 value = [2, 1] 3410->3411 3414 workclass_Public <= 0.5 entropy = 0.755 samples = 23 value = [5, 18] 3410->3414 3412 entropy = 0.0 samples = 1 value = [0, 1] 3411->3412 3413 entropy = 0.0 samples = 2 value = [2, 0] 3411->3413 3415 age <= 54.5 entropy = 0.874 samples = 17 value = [5, 12] 3414->3415 3430 entropy = 0.0 samples = 6 value = [0, 6] 3414->3430 3416 entropy = 0.0 samples = 1 value = [1, 0] 3415->3416 3417 age <= 56.0 entropy = 0.811 samples = 16 value = [4, 12] 3415->3417 3418 entropy = 0.0 samples = 3 value = [0, 3] 3417->3418 3419 age <= 60.5 entropy = 0.89 samples = 13 value = [4, 9] 3417->3419 3420 age <= 59.5 entropy = 0.971 samples = 5 value = [3, 2] 3419->3420 3427 age <= 73.0 entropy = 0.544 samples = 8 value = [1, 7] 3419->3427 3421 workclass_Self-emp <= 0.5 entropy = 1.0 samples = 4 value = [2, 2] 3420->3421 3426 entropy = 0.0 samples = 1 value = [1, 0] 3420->3426 3422 age <= 58.5 entropy = 0.918 samples = 3 value = [2, 1] 3421->3422 3425 entropy = 0.0 samples = 1 value = [0, 1] 3421->3425 3423 entropy = 0.0 samples = 2 value = [2, 0] 3422->3423 3424 entropy = 0.0 samples = 1 value = [0, 1] 3422->3424 3428 entropy = 0.0 samples = 6 value = [0, 6] 3427->3428 3429 entropy = 1.0 samples = 2 value = [1, 1] 3427->3429 3435 education <= 12.5 entropy = 0.902 samples = 44 value = [14, 30] 3434->3435 3476 age <= 56.5 entropy = 0.937 samples = 17 value = [11, 6] 3434->3476 3436 hours-per-week <= 37.5 entropy = 0.439 samples = 11 value = [1, 10] 3435->3436 3441 workclass_Self-emp <= 0.5 entropy = 0.967 samples = 33 value = [13, 20] 3435->3441 3437 workclass_Private <= 0.5 entropy = 0.918 samples = 3 value = [1, 2] 3436->3437 3440 entropy = 0.0 samples = 8 value = [0, 8] 3436->3440 3438 entropy = 0.0 samples = 2 value = [0, 2] 3437->3438 3439 entropy = 0.0 samples = 1 value = [1, 0] 3437->3439 3442 age <= 43.5 entropy = 0.938 samples = 31 value = [11, 20] 3441->3442 3475 entropy = 0.0 samples = 2 value = [2, 0] 3441->3475 3443 hours-per-week <= 38.5 entropy = 0.993 samples = 20 value = [9, 11] 3442->3443 3466 education <= 13.5 entropy = 0.684 samples = 11 value = [2, 9] 3442->3466 3444 age <= 37.5 entropy = 0.722 samples = 5 value = [1, 4] 3443->3444 3447 age <= 39.5 entropy = 0.997 samples = 15 value = [8, 7] 3443->3447 3445 entropy = 0.0 samples = 1 value = [1, 0] 3444->3445 3446 entropy = 0.0 samples = 4 value = [0, 4] 3444->3446 3448 education <= 13.5 entropy = 0.954 samples = 8 value = [3, 5] 3447->3448 3457 age <= 40.5 entropy = 0.863 samples = 7 value = [5, 2] 3447->3457 3449 age <= 37.5 entropy = 0.863 samples = 7 value = [2, 5] 3448->3449 3456 entropy = 0.0 samples = 1 value = [1, 0] 3448->3456 3450 entropy = 0.0 samples = 2 value = [0, 2] 3449->3450 3451 age <= 38.5 entropy = 0.971 samples = 5 value = [2, 3] 3449->3451 3452 workclass_Private <= 0.5 entropy = 1.0 samples = 4 value = [2, 2] 3451->3452 3455 entropy = 0.0 samples = 1 value = [0, 1] 3451->3455 3453 entropy = 0.0 samples = 1 value = [0, 1] 3452->3453 3454 entropy = 0.918 samples = 3 value = [2, 1] 3452->3454 3458 entropy = 0.0 samples = 2 value = [2, 0] 3457->3458 3459 age <= 42.5 entropy = 0.971 samples = 5 value = [3, 2] 3457->3459 3460 race_Black <= 0.5 entropy = 1.0 samples = 4 value = [2, 2] 3459->3460 3465 entropy = 0.0 samples = 1 value = [1, 0] 3459->3465 3461 education <= 14.0 entropy = 0.918 samples = 3 value = [1, 2] 3460->3461 3464 entropy = 0.0 samples = 1 value = [1, 0] 3460->3464 3462 entropy = 1.0 samples = 2 value = [1, 1] 3461->3462 3463 entropy = 0.0 samples = 1 value = [0, 1] 3461->3463 3467 entropy = 0.0 samples = 5 value = [0, 5] 3466->3467 3468 age <= 50.5 entropy = 0.918 samples = 6 value = [2, 4] 3466->3468 3469 age <= 46.5 entropy = 1.0 samples = 4 value = [2, 2] 3468->3469 3474 entropy = 0.0 samples = 2 value = [0, 2] 3468->3474 3470 entropy = 0.0 samples = 1 value = [0, 1] 3469->3470 3471 workclass_Private <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 3469->3471 3472 entropy = 0.0 samples = 1 value = [1, 0] 3471->3472 3473 entropy = 1.0 samples = 2 value = [1, 1] 3471->3473 3477 entropy = 0.0 samples = 5 value = [5, 0] 3476->3477 3478 workclass_Self-emp <= 0.5 entropy = 1.0 samples = 12 value = [6, 6] 3476->3478 3479 education <= 12.5 entropy = 0.971 samples = 10 value = [6, 4] 3478->3479 3494 entropy = 0.0 samples = 2 value = [0, 2] 3478->3494 3480 entropy = 0.0 samples = 2 value = [2, 0] 3479->3480 3481 age <= 67.0 entropy = 1.0 samples = 8 value = [4, 4] 3479->3481 3482 age <= 62.5 entropy = 0.985 samples = 7 value = [3, 4] 3481->3482 3493 entropy = 0.0 samples = 1 value = [1, 0] 3481->3493 3483 race_White <= 0.5 entropy = 1.0 samples = 6 value = [3, 3] 3482->3483 3492 entropy = 0.0 samples = 1 value = [0, 1] 3482->3492 3484 entropy = 0.0 samples = 1 value = [0, 1] 3483->3484 3485 age <= 57.5 entropy = 0.971 samples = 5 value = [3, 2] 3483->3485 3486 entropy = 0.0 samples = 1 value = [1, 0] 3485->3486 3487 age <= 58.5 entropy = 1.0 samples = 4 value = [2, 2] 3485->3487 3488 entropy = 0.0 samples = 1 value = [0, 1] 3487->3488 3489 workclass_Private <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 3487->3489 3490 entropy = 0.0 samples = 1 value = [1, 0] 3489->3490 3491 entropy = 1.0 samples = 2 value = [1, 1] 3489->3491 3496 entropy = 0.0 samples = 4 value = [4, 0] 3495->3496 3497 age <= 55.0 entropy = 1.0 samples = 28 value = [14, 14] 3495->3497 3498 education <= 13.5 entropy = 0.949 samples = 19 value = [7, 12] 3497->3498 3519 hours-per-week <= 33.5 entropy = 0.764 samples = 9 value = [7, 2] 3497->3519 3499 education <= 12.5 entropy = 1.0 samples = 12 value = [6, 6] 3498->3499 3516 age <= 38.5 entropy = 0.592 samples = 7 value = [1, 6] 3498->3516 3500 entropy = 0.0 samples = 1 value = [1, 0] 3499->3500 3501 age <= 39.0 entropy = 0.994 samples = 11 value = [5, 6] 3499->3501 3502 entropy = 0.0 samples = 1 value = [1, 0] 3501->3502 3503 age <= 44.5 entropy = 0.971 samples = 10 value = [4, 6] 3501->3503 3504 entropy = 0.0 samples = 2 value = [0, 2] 3503->3504 3505 age <= 46.0 entropy = 1.0 samples = 8 value = [4, 4] 3503->3505 3506 entropy = 0.0 samples = 1 value = [1, 0] 3505->3506 3507 age <= 52.0 entropy = 0.985 samples = 7 value = [3, 4] 3505->3507 3508 age <= 50.0 entropy = 0.918 samples = 6 value = [2, 4] 3507->3508 3515 entropy = 0.0 samples = 1 value = [1, 0] 3507->3515 3509 workclass_Private <= 0.5 entropy = 0.971 samples = 5 value = [2, 3] 3508->3509 3514 entropy = 0.0 samples = 1 value = [0, 1] 3508->3514 3510 entropy = 0.0 samples = 1 value = [1, 0] 3509->3510 3511 age <= 48.5 entropy = 0.811 samples = 4 value = [1, 3] 3509->3511 3512 entropy = 0.0 samples = 3 value = [0, 3] 3511->3512 3513 entropy = 0.0 samples = 1 value = [1, 0] 3511->3513 3517 entropy = 1.0 samples = 2 value = [1, 1] 3516->3517 3518 entropy = 0.0 samples = 5 value = [0, 5] 3516->3518 3520 entropy = 0.0 samples = 1 value = [0, 1] 3519->3520 3521 education <= 14.5 entropy = 0.544 samples = 8 value = [7, 1] 3519->3521 3522 entropy = 0.0 samples = 5 value = [5, 0] 3521->3522 3523 education <= 15.5 entropy = 0.918 samples = 3 value = [2, 1] 3521->3523 3524 entropy = 0.0 samples = 1 value = [0, 1] 3523->3524 3525 entropy = 0.0 samples = 2 value = [2, 0] 3523->3525 3527 hours-per-week <= 48.5 entropy = 0.958 samples = 137 value = [52, 85] 3526->3527 3660 education <= 14.5 entropy = 0.716 samples = 584 value = [115, 469] 3526->3660 3528 sex_Female <= 0.5 entropy = 0.828 samples = 46 value = [12, 34] 3527->3528 3563 age <= 24.5 entropy = 0.989 samples = 91 value = [40, 51] 3527->3563 3529 education <= 13.5 entropy = 0.881 samples = 40 value = [12, 28] 3528->3529 3562 entropy = 0.0 samples = 6 value = [0, 6] 3528->3562 3530 age <= 27.5 entropy = 0.928 samples = 35 value = [12, 23] 3529->3530 3561 entropy = 0.0 samples = 5 value = [0, 5] 3529->3561 3531 age <= 24.5 entropy = 0.544 samples = 8 value = [1, 7] 3530->3531 3536 workclass_Self-emp <= 0.5 entropy = 0.975 samples = 27 value = [11, 16] 3530->3536 3532 hours-per-week <= 44.5 entropy = 0.918 samples = 3 value = [1, 2] 3531->3532 3535 entropy = 0.0 samples = 5 value = [0, 5] 3531->3535 3533 entropy = 0.0 samples = 1 value = [0, 1] 3532->3533 3534 entropy = 1.0 samples = 2 value = [1, 1] 3532->3534 3537 education <= 12.5 entropy = 0.99 samples = 25 value = [11, 14] 3536->3537 3560 entropy = 0.0 samples = 2 value = [0, 2] 3536->3560 3538 age <= 31.5 entropy = 0.918 samples = 6 value = [4, 2] 3537->3538 3543 age <= 31.0 entropy = 0.949 samples = 19 value = [7, 12] 3537->3543 3539 workclass_Private <= 0.5 entropy = 0.918 samples = 3 value = [1, 2] 3538->3539 3542 entropy = 0.0 samples = 3 value = [3, 0] 3538->3542 3540 entropy = 0.0 samples = 1 value = [1, 0] 3539->3540 3541 entropy = 0.0 samples = 2 value = [0, 2] 3539->3541 3544 hours-per-week <= 42.5 entropy = 0.994 samples = 11 value = [6, 5] 3543->3544 3553 age <= 32.5 entropy = 0.544 samples = 8 value = [1, 7] 3543->3553 3545 entropy = 0.0 samples = 1 value = [0, 1] 3544->3545 3546 hours-per-week <= 44.0 entropy = 0.971 samples = 10 value = [6, 4] 3544->3546 3547 entropy = 0.0 samples = 1 value = [1, 0] 3546->3547 3548 age <= 29.5 entropy = 0.991 samples = 9 value = [5, 4] 3546->3548 3549 age <= 28.5 entropy = 0.918 samples = 6 value = [4, 2] 3548->3549 3552 entropy = 0.918 samples = 3 value = [1, 2] 3548->3552 3550 entropy = 0.971 samples = 5 value = [3, 2] 3549->3550 3551 entropy = 0.0 samples = 1 value = [1, 0] 3549->3551 3554 entropy = 0.0 samples = 3 value = [0, 3] 3553->3554 3555 workclass_Public <= 0.5 entropy = 0.722 samples = 5 value = [1, 4] 3553->3555 3556 hours-per-week <= 46.0 entropy = 0.918 samples = 3 value = [1, 2] 3555->3556 3559 entropy = 0.0 samples = 2 value = [0, 2] 3555->3559 3557 entropy = 1.0 samples = 2 value = [1, 1] 3556->3557 3558 entropy = 0.0 samples = 1 value = [0, 1] 3556->3558 3564 entropy = 0.0 samples = 2 value = [2, 0] 3563->3564 3565 race_Black <= 0.5 entropy = 0.985 samples = 89 value = [38, 51] 3563->3565 3566 age <= 29.5 entropy = 0.988 samples = 87 value = [38, 49] 3565->3566 3659 entropy = 0.0 samples = 2 value = [0, 2] 3565->3659 3567 race_Asian <= 0.5 entropy = 0.993 samples = 31 value = [17, 14] 3566->3567 3610 hours-per-week <= 72.5 entropy = 0.954 samples = 56 value = [21, 35] 3566->3610 3568 hours-per-week <= 89.5 entropy = 0.999 samples = 29 value = [15, 14] 3567->3568 3609 entropy = 0.0 samples = 2 value = [2, 0] 3567->3609 3569 education <= 15.5 entropy = 0.996 samples = 28 value = [15, 13] 3568->3569 3608 entropy = 0.0 samples = 1 value = [0, 1] 3568->3608 3570 age <= 28.5 entropy = 0.991 samples = 27 value = [15, 12] 3569->3570 3607 entropy = 0.0 samples = 1 value = [0, 1] 3569->3607 3571 workclass_Self-emp <= 0.5 entropy = 0.998 samples = 17 value = [8, 9] 3570->3571 3592 education <= 13.5 entropy = 0.881 samples = 10 value = [7, 3] 3570->3592 3572 education <= 12.5 entropy = 1.0 samples = 16 value = [8, 8] 3571->3572 3591 entropy = 0.0 samples = 1 value = [0, 1] 3571->3591 3573 entropy = 0.0 samples = 1 value = [1, 0] 3572->3573 3574 hours-per-week <= 57.5 entropy = 0.997 samples = 15 value = [7, 8] 3572->3574 3575 workclass_Private <= 0.5 entropy = 0.994 samples = 11 value = [6, 5] 3574->3575 3588 workclass_Public <= 0.5 entropy = 0.811 samples = 4 value = [1, 3] 3574->3588 3576 hours-per-week <= 52.5 entropy = 0.918 samples = 3 value = [1, 2] 3575->3576 3579 sex_Female <= 0.5 entropy = 0.954 samples = 8 value = [5, 3] 3575->3579 3577 entropy = 0.0 samples = 2 value = [0, 2] 3576->3577 3578 entropy = 0.0 samples = 1 value = [1, 0] 3576->3578 3580 hours-per-week <= 52.5 entropy = 0.863 samples = 7 value = [5, 2] 3579->3580 3587 entropy = 0.0 samples = 1 value = [0, 1] 3579->3587 3581 age <= 26.5 entropy = 0.65 samples = 6 value = [5, 1] 3580->3581 3586 entropy = 0.0 samples = 1 value = [0, 1] 3580->3586 3582 age <= 25.5 entropy = 0.918 samples = 3 value = [2, 1] 3581->3582 3585 entropy = 0.0 samples = 3 value = [3, 0] 3581->3585 3583 entropy = 0.0 samples = 1 value = [1, 0] 3582->3583 3584 entropy = 1.0 samples = 2 value = [1, 1] 3582->3584 3589 entropy = 0.0 samples = 3 value = [0, 3] 3588->3589 3590 entropy = 0.0 samples = 1 value = [1, 0] 3588->3590 3593 workclass_Public <= 0.5 entropy = 0.954 samples = 8 value = [5, 3] 3592->3593 3606 entropy = 0.0 samples = 2 value = [2, 0] 3592->3606 3594 hours-per-week <= 65.0 entropy = 0.863 samples = 7 value = [5, 2] 3593->3594 3605 entropy = 0.0 samples = 1 value = [0, 1] 3593->3605 3595 sex_Female <= 0.5 entropy = 0.65 samples = 6 value = [5, 1] 3594->3595 3604 entropy = 0.0 samples = 1 value = [0, 1] 3594->3604 3596 workclass_Self-emp <= 0.5 entropy = 0.722 samples = 5 value = [4, 1] 3595->3596 3603 entropy = 0.0 samples = 1 value = [1, 0] 3595->3603 3597 hours-per-week <= 55.0 entropy = 0.811 samples = 4 value = [3, 1] 3596->3597 3602 entropy = 0.0 samples = 1 value = [1, 0] 3596->3602 3598 education <= 12.5 entropy = 0.918 samples = 3 value = [2, 1] 3597->3598 3601 entropy = 0.0 samples = 1 value = [1, 0] 3597->3601 3599 entropy = 1.0 samples = 2 value = [1, 1] 3598->3599 3600 entropy = 0.0 samples = 1 value = [1, 0] 3598->3600 3611 hours-per-week <= 62.5 entropy = 0.964 samples = 54 value = [21, 33] 3610->3611 3658 entropy = 0.0 samples = 2 value = [0, 2] 3610->3658 3612 workclass_Public <= 0.5 entropy = 0.931 samples = 49 value = [17, 32] 3611->3612 3653 age <= 30.5 entropy = 0.722 samples = 5 value = [4, 1] 3611->3653 3613 age <= 31.5 entropy = 0.893 samples = 42 value = [13, 29] 3612->3613 3646 age <= 31.5 entropy = 0.985 samples = 7 value = [4, 3] 3612->3646 3614 education <= 13.5 entropy = 0.991 samples = 18 value = [8, 10] 3613->3614 3631 hours-per-week <= 57.5 entropy = 0.738 samples = 24 value = [5, 19] 3613->3631 3615 hours-per-week <= 53.5 entropy = 0.918 samples = 15 value = [5, 10] 3614->3615 3630 entropy = 0.0 samples = 3 value = [3, 0] 3614->3630 3616 age <= 30.5 entropy = 0.503 samples = 9 value = [1, 8] 3615->3616 3623 education <= 12.5 entropy = 0.918 samples = 6 value = [4, 2] 3615->3623 3617 education <= 12.5 entropy = 0.65 samples = 6 value = [1, 5] 3616->3617 3622 entropy = 0.0 samples = 3 value = [0, 3] 3616->3622 3618 entropy = 0.0 samples = 2 value = [0, 2] 3617->3618 3619 workclass_Private <= 0.5 entropy = 0.811 samples = 4 value = [1, 3] 3617->3619 3620 entropy = 0.0 samples = 1 value = [0, 1] 3619->3620 3621 entropy = 0.918 samples = 3 value = [1, 2] 3619->3621 3624 entropy = 0.0 samples = 2 value = [2, 0] 3623->3624 3625 workclass_Private <= 0.5 entropy = 1.0 samples = 4 value = [2, 2] 3623->3625 3626 entropy = 0.0 samples = 1 value = [1, 0] 3625->3626 3627 hours-per-week <= 57.5 entropy = 0.918 samples = 3 value = [1, 2] 3625->3627 3628 entropy = 1.0 samples = 2 value = [1, 1] 3627->3628 3629 entropy = 0.0 samples = 1 value = [0, 1] 3627->3629 3632 workclass_Private <= 0.5 entropy = 0.896 samples = 16 value = [5, 11] 3631->3632 3645 entropy = 0.0 samples = 8 value = [0, 8] 3631->3645 3633 entropy = 0.0 samples = 3 value = [0, 3] 3632->3633 3634 education <= 14.5 entropy = 0.961 samples = 13 value = [5, 8] 3632->3634 3635 age <= 32.5 entropy = 0.994 samples = 11 value = [5, 6] 3634->3635 3644 entropy = 0.0 samples = 2 value = [0, 2] 3634->3644 3636 hours-per-week <= 52.5 entropy = 0.811 samples = 4 value = [1, 3] 3635->3636 3639 hours-per-week <= 52.5 entropy = 0.985 samples = 7 value = [4, 3] 3635->3639 3637 entropy = 0.918 samples = 3 value = [1, 2] 3636->3637 3638 entropy = 0.0 samples = 1 value = [0, 1] 3636->3638 3640 education <= 13.5 entropy = 0.971 samples = 5 value = [3, 2] 3639->3640 3643 entropy = 1.0 samples = 2 value = [1, 1] 3639->3643 3641 entropy = 0.918 samples = 3 value = [2, 1] 3640->3641 3642 entropy = 1.0 samples = 2 value = [1, 1] 3640->3642 3647 entropy = 0.0 samples = 2 value = [0, 2] 3646->3647 3648 age <= 32.5 entropy = 0.722 samples = 5 value = [4, 1] 3646->3648 3649 entropy = 0.0 samples = 2 value = [2, 0] 3648->3649 3650 hours-per-week <= 55.0 entropy = 0.918 samples = 3 value = [2, 1] 3648->3650 3651 entropy = 1.0 samples = 2 value = [1, 1] 3650->3651 3652 entropy = 0.0 samples = 1 value = [1, 0] 3650->3652 3654 entropy = 0.0 samples = 2 value = [2, 0] 3653->3654 3655 age <= 31.5 entropy = 0.918 samples = 3 value = [2, 1] 3653->3655 3656 entropy = 0.0 samples = 1 value = [0, 1] 3655->3656 3657 entropy = 0.0 samples = 2 value = [2, 0] 3655->3657 3661 race_White <= 0.5 entropy = 0.776 samples = 472 value = [108, 364] 3660->3661 3976 age <= 56.5 entropy = 0.337 samples = 112 value = [7, 105] 3660->3976 3662 workclass_Public <= 0.5 entropy = 0.99 samples = 25 value = [14, 11] 3661->3662 3681 hours-per-week <= 85.0 entropy = 0.742 samples = 447 value = [94, 353] 3661->3681 3663 workclass_Private <= 0.5 entropy = 0.946 samples = 22 value = [14, 8] 3662->3663 3680 entropy = 0.0 samples = 3 value = [0, 3] 3662->3680 3664 race_Hispanic <= 0.5 entropy = 0.544 samples = 8 value = [7, 1] 3663->3664 3667 age <= 42.5 entropy = 1.0 samples = 14 value = [7, 7] 3663->3667 3665 entropy = 0.0 samples = 6 value = [6, 0] 3664->3665 3666 entropy = 1.0 samples = 2 value = [1, 1] 3664->3666 3668 education <= 13.5 entropy = 0.863 samples = 7 value = [2, 5] 3667->3668 3675 race_Black <= 0.5 entropy = 0.863 samples = 7 value = [5, 2] 3667->3675 3669 hours-per-week <= 47.5 entropy = 0.971 samples = 5 value = [2, 3] 3668->3669 3674 entropy = 0.0 samples = 2 value = [0, 2] 3668->3674 3670 entropy = 0.0 samples = 1 value = [1, 0] 3669->3670 3671 age <= 39.5 entropy = 0.811 samples = 4 value = [1, 3] 3669->3671 3672 entropy = 0.0 samples = 2 value = [0, 2] 3671->3672 3673 entropy = 1.0 samples = 2 value = [1, 1] 3671->3673 3676 entropy = 0.0 samples = 4 value = [4, 0] 3675->3676 3677 age <= 46.5 entropy = 0.918 samples = 3 value = [1, 2] 3675->3677 3678 entropy = 0.0 samples = 1 value = [0, 1] 3677->3678 3679 entropy = 1.0 samples = 2 value = [1, 1] 3677->3679 3682 age <= 41.5 entropy = 0.729 samples = 442 value = [90, 352] 3681->3682 3973 age <= 40.5 entropy = 0.722 samples = 5 value = [4, 1] 3681->3973 3683 age <= 40.5 entropy = 0.6 samples = 178 value = [26, 152] 3682->3683 3774 workclass_Private <= 0.5 entropy = 0.799 samples = 264 value = [64, 200] 3682->3774 3684 education <= 12.5 entropy = 0.635 samples = 162 value = [26, 136] 3683->3684 3773 entropy = 0.0 samples = 16 value = [0, 16] 3683->3773 3685 entropy = 0.0 samples = 12 value = [0, 12] 3684->3685 3686 hours-per-week <= 57.5 entropy = 0.665 samples = 150 value = [26, 124] 3684->3686 3687 workclass_Public <= 0.5 entropy = 0.592 samples = 112 value = [16, 96] 3686->3687 3746 education <= 13.5 entropy = 0.831 samples = 38 value = [10, 28] 3686->3746 3688 hours-per-week <= 49.0 entropy = 0.642 samples = 98 value = [16, 82] 3687->3688 3745 entropy = 0.0 samples = 14 value = [0, 14] 3687->3745 3689 hours-per-week <= 44.0 entropy = 0.811 samples = 36 value = [9, 27] 3688->3689 3718 age <= 38.225 entropy = 0.509 samples = 62 value = [7, 55] 3688->3718 3690 entropy = 0.0 samples = 3 value = [0, 3] 3689->3690 3691 age <= 35.5 entropy = 0.845 samples = 33 value = [9, 24] 3689->3691 3692 hours-per-week <= 47.5 entropy = 0.684 samples = 11 value = [2, 9] 3691->3692 3697 hours-per-week <= 45.5 entropy = 0.902 samples = 22 value = [7, 15] 3691->3697 3693 age <= 34.5 entropy = 0.469 samples = 10 value = [1, 9] 3692->3693 3696 entropy = 0.0 samples = 1 value = [1, 0] 3692->3696 3694 entropy = 0.811 samples = 4 value = [1, 3] 3693->3694 3695 entropy = 0.0 samples = 6 value = [0, 6] 3693->3695 3698 sex_Male <= 0.5 entropy = 0.949 samples = 19 value = [7, 12] 3697->3698 3717 entropy = 0.0 samples = 3 value = [0, 3] 3697->3717 3699 entropy = 0.0 samples = 1 value = [0, 1] 3698->3699 3700 workclass_Self-emp <= 0.5 entropy = 0.964 samples = 18 value = [7, 11] 3698->3700 3701 age <= 39.5 entropy = 0.918 samples = 15 value = [5, 10] 3700->3701 3714 age <= 39.5 entropy = 0.918 samples = 3 value = [2, 1] 3700->3714 3702 age <= 38.5 entropy = 0.863 samples = 14 value = [4, 10] 3701->3702 3713 entropy = 0.0 samples = 1 value = [1, 0] 3701->3713 3703 education <= 13.5 entropy = 0.918 samples = 12 value = [4, 8] 3702->3703 3712 entropy = 0.0 samples = 2 value = [0, 2] 3702->3712 3704 age <= 36.5 entropy = 0.811 samples = 8 value = [2, 6] 3703->3704 3707 age <= 36.5 entropy = 1.0 samples = 4 value = [2, 2] 3703->3707 3705 entropy = 1.0 samples = 4 value = [2, 2] 3704->3705 3706 entropy = 0.0 samples = 4 value = [0, 4] 3704->3706 3708 entropy = 0.0 samples = 1 value = [0, 1] 3707->3708 3709 age <= 37.5 entropy = 0.918 samples = 3 value = [2, 1] 3707->3709 3710 entropy = 1.0 samples = 2 value = [1, 1] 3709->3710 3711 entropy = 0.0 samples = 1 value = [1, 0] 3709->3711 3715 entropy = 0.0 samples = 2 value = [2, 0] 3714->3715 3716 entropy = 0.0 samples = 1 value = [0, 1] 3714->3716 3719 age <= 34.5 entropy = 0.607 samples = 47 value = [7, 40] 3718->3719 3744 entropy = 0.0 samples = 15 value = [0, 15] 3718->3744 3720 entropy = 0.0 samples = 10 value = [0, 10] 3719->3720 3721 education <= 13.5 entropy = 0.7 samples = 37 value = [7, 30] 3719->3721 3722 workclass_Private <= 0.5 entropy = 0.746 samples = 33 value = [7, 26] 3721->3722 3743 entropy = 0.0 samples = 4 value = [0, 4] 3721->3743 3723 entropy = 0.0 samples = 2 value = [0, 2] 3722->3723 3724 age <= 37.5 entropy = 0.771 samples = 31 value = [7, 24] 3722->3724 3725 hours-per-week <= 53.5 entropy = 0.845 samples = 22 value = [6, 16] 3724->3725 3740 hours-per-week <= 52.5 entropy = 0.503 samples = 9 value = [1, 8] 3724->3740 3726 hours-per-week <= 51.0 entropy = 0.764 samples = 18 value = [4, 14] 3725->3726 3735 age <= 36.5 entropy = 1.0 samples = 4 value = [2, 2] 3725->3735 3727 sex_Female <= 0.5 entropy = 0.811 samples = 16 value = [4, 12] 3726->3727 3734 entropy = 0.0 samples = 2 value = [0, 2] 3726->3734 3728 age <= 35.5 entropy = 0.837 samples = 15 value = [4, 11] 3727->3728 3733 entropy = 0.0 samples = 1 value = [0, 1] 3727->3733 3729 entropy = 0.918 samples = 3 value = [1, 2] 3728->3729 3730 age <= 36.5 entropy = 0.811 samples = 12 value = [3, 9] 3728->3730 3731 entropy = 0.722 samples = 5 value = [1, 4] 3730->3731 3732 entropy = 0.863 samples = 7 value = [2, 5] 3730->3732 3736 age <= 35.5 entropy = 0.918 samples = 3 value = [2, 1] 3735->3736 3739 entropy = 0.0 samples = 1 value = [0, 1] 3735->3739 3737 entropy = 1.0 samples = 2 value = [1, 1] 3736->3737 3738 entropy = 0.0 samples = 1 value = [1, 0] 3736->3738 3741 entropy = 0.592 samples = 7 value = [1, 6] 3740->3741 3742 entropy = 0.0 samples = 2 value = [0, 2] 3740->3742 3747 sex_Female <= 0.5 entropy = 0.94 samples = 28 value = [10, 18] 3746->3747 3772 entropy = 0.0 samples = 10 value = [0, 10] 3746->3772 3748 hours-per-week <= 75.0 entropy = 0.918 samples = 27 value = [9, 18] 3747->3748 3771 entropy = 0.0 samples = 1 value = [1, 0] 3747->3771 3749 hours-per-week <= 62.5 entropy = 0.943 samples = 25 value = [9, 16] 3748->3749 3770 entropy = 0.0 samples = 2 value = [0, 2] 3748->3770 3750 age <= 39.5 entropy = 0.863 samples = 21 value = [6, 15] 3749->3750 3767 age <= 34.5 entropy = 0.811 samples = 4 value = [3, 1] 3749->3767 3751 workclass_Public <= 0.5 entropy = 0.764 samples = 18 value = [4, 14] 3750->3751 3764 workclass_Self-emp <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 3750->3764 3752 age <= 34.5 entropy = 0.696 samples = 16 value = [3, 13] 3751->3752 3763 entropy = 1.0 samples = 2 value = [1, 1] 3751->3763 3753 entropy = 0.0 samples = 5 value = [0, 5] 3752->3753 3754 age <= 35.5 entropy = 0.845 samples = 11 value = [3, 8] 3752->3754 3755 workclass_Self-emp <= 0.5 entropy = 1.0 samples = 4 value = [2, 2] 3754->3755 3758 age <= 38.725 entropy = 0.592 samples = 7 value = [1, 6] 3754->3758 3756 entropy = 0.918 samples = 3 value = [1, 2] 3755->3756 3757 entropy = 0.0 samples = 1 value = [1, 0] 3755->3757 3759 entropy = 0.0 samples = 4 value = [0, 4] 3758->3759 3760 workclass_Self-emp <= 0.5 entropy = 0.918 samples = 3 value = [1, 2] 3758->3760 3761 entropy = 0.0 samples = 1 value = [1, 0] 3760->3761 3762 entropy = 0.0 samples = 2 value = [0, 2] 3760->3762 3765 entropy = 1.0 samples = 2 value = [1, 1] 3764->3765 3766 entropy = 0.0 samples = 1 value = [1, 0] 3764->3766 3768 entropy = 0.0 samples = 2 value = [2, 0] 3767->3768 3769 entropy = 1.0 samples = 2 value = [1, 1] 3767->3769 3775 education <= 12.5 entropy = 0.879 samples = 114 value = [34, 80] 3774->3775 3870 age <= 80.0 entropy = 0.722 samples = 150 value = [30, 120] 3774->3870 3776 entropy = 0.0 samples = 2 value = [2, 0] 3775->3776 3777 education <= 13.5 entropy = 0.863 samples = 112 value = [32, 80] 3775->3777 3778 hours-per-week <= 53.5 entropy = 0.909 samples = 71 value = [23, 48] 3777->3778 3847 age <= 43.5 entropy = 0.759 samples = 41 value = [9, 32] 3777->3847 3779 age <= 82.5 entropy = 0.811 samples = 44 value = [11, 33] 3778->3779 3820 workclass_Self-emp <= 0.5 entropy = 0.991 samples = 27 value = [12, 15] 3778->3820 3780 age <= 65.5 entropy = 0.782 samples = 43 value = [10, 33] 3779->3780 3819 entropy = 0.0 samples = 1 value = [1, 0] 3779->3819 3781 age <= 61.5 entropy = 0.801 samples = 41 value = [10, 31] 3780->3781 3818 entropy = 0.0 samples = 2 value = [0, 2] 3780->3818 3782 sex_Female <= 0.5 entropy = 0.769 samples = 40 value = [9, 31] 3781->3782 3817 entropy = 0.0 samples = 1 value = [1, 0] 3781->3817 3783 age <= 60.5 entropy = 0.822 samples = 35 value = [9, 26] 3782->3783 3816 entropy = 0.0 samples = 5 value = [0, 5] 3782->3816 3784 age <= 58.0 entropy = 0.845 samples = 33 value = [9, 24] 3783->3784 3815 entropy = 0.0 samples = 2 value = [0, 2] 3783->3815 3785 age <= 51.5 entropy = 0.811 samples = 32 value = [8, 24] 3784->3785 3814 entropy = 0.0 samples = 1 value = [1, 0] 3784->3814 3786 hours-per-week <= 46.5 entropy = 0.877 samples = 27 value = [8, 19] 3785->3786 3813 entropy = 0.0 samples = 5 value = [0, 5] 3785->3813 3787 age <= 43.5 entropy = 0.971 samples = 10 value = [4, 6] 3786->3787 3798 age <= 42.5 entropy = 0.787 samples = 17 value = [4, 13] 3786->3798 3788 entropy = 0.0 samples = 1 value = [1, 0] 3787->3788 3789 age <= 50.5 entropy = 0.918 samples = 9 value = [3, 6] 3787->3789 3790 age <= 47.5 entropy = 0.811 samples = 8 value = [2, 6] 3789->3790 3797 entropy = 0.0 samples = 1 value = [1, 0] 3789->3797 3791 workclass_Self-emp <= 0.5 entropy = 0.65 samples = 6 value = [1, 5] 3790->3791 3796 entropy = 1.0 samples = 2 value = [1, 1] 3790->3796 3792 entropy = 0.0 samples = 3 value = [0, 3] 3791->3792 3793 hours-per-week <= 43.5 entropy = 0.918 samples = 3 value = [1, 2] 3791->3793 3794 entropy = 0.0 samples = 1 value = [0, 1] 3793->3794 3795 entropy = 1.0 samples = 2 value = [1, 1] 3793->3795 3799 entropy = 0.0 samples = 4 value = [0, 4] 3798->3799 3800 age <= 43.5 entropy = 0.89 samples = 13 value = [4, 9] 3798->3800 3801 workclass_Self-emp <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 3800->3801 3804 age <= 45.5 entropy = 0.722 samples = 10 value = [2, 8] 3800->3804 3802 entropy = 1.0 samples = 2 value = [1, 1] 3801->3802 3803 entropy = 0.0 samples = 1 value = [1, 0] 3801->3803 3805 entropy = 0.0 samples = 4 value = [0, 4] 3804->3805 3806 age <= 46.5 entropy = 0.918 samples = 6 value = [2, 4] 3804->3806 3807 entropy = 0.0 samples = 1 value = [1, 0] 3806->3807 3808 age <= 48.5 entropy = 0.722 samples = 5 value = [1, 4] 3806->3808 3809 entropy = 0.0 samples = 2 value = [0, 2] 3808->3809 3810 hours-per-week <= 51.0 entropy = 0.918 samples = 3 value = [1, 2] 3808->3810 3811 entropy = 1.0 samples = 2 value = [1, 1] 3810->3811 3812 entropy = 0.0 samples = 1 value = [0, 1] 3810->3812 3821 entropy = 0.0 samples = 3 value = [3, 0] 3820->3821 3822 age <= 59.0 entropy = 0.954 samples = 24 value = [9, 15] 3820->3822 3823 age <= 46.5 entropy = 0.993 samples = 20 value = [9, 11] 3822->3823 3846 entropy = 0.0 samples = 4 value = [0, 4] 3822->3846 3824 age <= 42.5 entropy = 0.811 samples = 8 value = [2, 6] 3823->3824 3831 hours-per-week <= 57.5 entropy = 0.98 samples = 12 value = [7, 5] 3823->3831 3825 hours-per-week <= 75.0 entropy = 0.971 samples = 5 value = [2, 3] 3824->3825 3830 entropy = 0.0 samples = 3 value = [0, 3] 3824->3830 3826 hours-per-week <= 65.0 entropy = 1.0 samples = 4 value = [2, 2] 3825->3826 3829 entropy = 0.0 samples = 1 value = [0, 1] 3825->3829 3827 entropy = 1.0 samples = 2 value = [1, 1] 3826->3827 3828 entropy = 1.0 samples = 2 value = [1, 1] 3826->3828 3832 entropy = 0.0 samples = 1 value = [1, 0] 3831->3832 3833 age <= 55.5 entropy = 0.994 samples = 11 value = [6, 5] 3831->3833 3834 age <= 53.5 entropy = 1.0 samples = 10 value = [5, 5] 3833->3834 3845 entropy = 0.0 samples = 1 value = [1, 0] 3833->3845 3835 hours-per-week <= 67.5 entropy = 0.991 samples = 9 value = [5, 4] 3834->3835 3844 entropy = 0.0 samples = 1 value = [0, 1] 3834->3844 3836 hours-per-week <= 62.5 entropy = 1.0 samples = 8 value = [4, 4] 3835->3836 3843 entropy = 0.0 samples = 1 value = [1, 0] 3835->3843 3837 age <= 48.5 entropy = 0.985 samples = 7 value = [4, 3] 3836->3837 3842 entropy = 0.0 samples = 1 value = [0, 1] 3836->3842 3838 entropy = 0.918 samples = 3 value = [2, 1] 3837->3838 3839 age <= 51.0 entropy = 1.0 samples = 4 value = [2, 2] 3837->3839 3840 entropy = 1.0 samples = 2 value = [1, 1] 3839->3840 3841 entropy = 1.0 samples = 2 value = [1, 1] 3839->3841 3848 workclass_Public <= 0.5 entropy = 0.971 samples = 5 value = [3, 2] 3847->3848 3851 age <= 49.5 entropy = 0.65 samples = 36 value = [6, 30] 3847->3851 3849 entropy = 0.0 samples = 3 value = [3, 0] 3848->3849 3850 entropy = 0.0 samples = 2 value = [0, 2] 3848->3850 3852 entropy = 0.0 samples = 14 value = [0, 14] 3851->3852 3853 sex_Female <= 0.5 entropy = 0.845 samples = 22 value = [6, 16] 3851->3853 3854 age <= 50.5 entropy = 0.722 samples = 20 value = [4, 16] 3853->3854 3869 entropy = 0.0 samples = 2 value = [2, 0] 3853->3869 3855 entropy = 0.0 samples = 1 value = [1, 0] 3854->3855 3856 age <= 57.5 entropy = 0.629 samples = 19 value = [3, 16] 3854->3856 3857 age <= 54.5 entropy = 0.779 samples = 13 value = [3, 10] 3856->3857 3868 entropy = 0.0 samples = 6 value = [0, 6] 3856->3868 3858 hours-per-week <= 55.0 entropy = 0.918 samples = 6 value = [2, 4] 3857->3858 3865 hours-per-week <= 57.5 entropy = 0.592 samples = 7 value = [1, 6] 3857->3865 3859 workclass_Self-emp <= 0.5 entropy = 0.971 samples = 5 value = [2, 3] 3858->3859 3864 entropy = 0.0 samples = 1 value = [0, 1] 3858->3864 3860 entropy = 0.0 samples = 1 value = [1, 0] 3859->3860 3861 age <= 52.5 entropy = 0.811 samples = 4 value = [1, 3] 3859->3861 3862 entropy = 0.0 samples = 2 value = [0, 2] 3861->3862 3863 entropy = 1.0 samples = 2 value = [1, 1] 3861->3863 3866 entropy = 0.0 samples = 5 value = [0, 5] 3865->3866 3867 entropy = 1.0 samples = 2 value = [1, 1] 3865->3867 3871 age <= 50.5 entropy = 0.711 samples = 149 value = [29, 120] 3870->3871 3972 entropy = 0.0 samples = 1 value = [1, 0] 3870->3972 3872 hours-per-week <= 67.5 entropy = 0.764 samples = 99 value = [22, 77] 3871->3872 3943 hours-per-week <= 49.0 entropy = 0.584 samples = 50 value = [7, 43] 3871->3943 3873 education <= 12.5 entropy = 0.75 samples = 98 value = [21, 77] 3872->3873 3942 entropy = 0.0 samples = 1 value = [1, 0] 3872->3942 3874 age <= 46.5 entropy = 0.971 samples = 10 value = [4, 6] 3873->3874 3883 age <= 44.5 entropy = 0.708 samples = 88 value = [17, 71] 3873->3883 3875 entropy = 0.0 samples = 4 value = [0, 4] 3874->3875 3876 hours-per-week <= 47.5 entropy = 0.918 samples = 6 value = [4, 2] 3874->3876 3877 entropy = 0.0 samples = 2 value = [2, 0] 3876->3877 3878 age <= 49.5 entropy = 1.0 samples = 4 value = [2, 2] 3876->3878 3879 age <= 48.5 entropy = 0.918 samples = 3 value = [1, 2] 3878->3879 3882 entropy = 0.0 samples = 1 value = [1, 0] 3878->3882 3880 entropy = 1.0 samples = 2 value = [1, 1] 3879->3880 3881 entropy = 0.0 samples = 1 value = [0, 1] 3879->3881 3884 education <= 13.5 entropy = 0.822 samples = 35 value = [9, 26] 3883->3884 3911 hours-per-week <= 44.5 entropy = 0.612 samples = 53 value = [8, 45] 3883->3911 3885 hours-per-week <= 62.5 entropy = 0.89 samples = 26 value = [8, 18] 3884->3885 3904 hours-per-week <= 47.5 entropy = 0.503 samples = 9 value = [1, 8] 3884->3904 3886 hours-per-week <= 56.5 entropy = 0.904 samples = 25 value = [8, 17] 3885->3886 3903 entropy = 0.0 samples = 1 value = [0, 1] 3885->3903 3887 age <= 42.5 entropy = 0.874 samples = 17 value = [5, 12] 3886->3887 3896 hours-per-week <= 59.0 entropy = 0.954 samples = 8 value = [3, 5] 3886->3896 3888 entropy = 0.0 samples = 4 value = [0, 4] 3887->3888 3889 hours-per-week <= 47.5 entropy = 0.961 samples = 13 value = [5, 8] 3887->3889 3890 age <= 43.5 entropy = 0.918 samples = 3 value = [2, 1] 3889->3890 3893 age <= 43.5 entropy = 0.881 samples = 10 value = [3, 7] 3889->3893 3891 entropy = 1.0 samples = 2 value = [1, 1] 3890->3891 3892 entropy = 0.0 samples = 1 value = [1, 0] 3890->3892 3894 entropy = 0.971 samples = 5 value = [2, 3] 3893->3894 3895 entropy = 0.722 samples = 5 value = [1, 4] 3893->3895 3897 entropy = 0.0 samples = 1 value = [1, 0] 3896->3897 3898 age <= 42.5 entropy = 0.863 samples = 7 value = [2, 5] 3896->3898 3899 entropy = 1.0 samples = 2 value = [1, 1] 3898->3899 3900 age <= 43.5 entropy = 0.722 samples = 5 value = [1, 4] 3898->3900 3901 entropy = 0.0 samples = 1 value = [0, 1] 3900->3901 3902 entropy = 0.811 samples = 4 value = [1, 3] 3900->3902 3905 entropy = 0.0 samples = 4 value = [0, 4] 3904->3905 3906 age <= 42.5 entropy = 0.722 samples = 5 value = [1, 4] 3904->3906 3907 entropy = 0.0 samples = 2 value = [0, 2] 3906->3907 3908 age <= 43.5 entropy = 0.918 samples = 3 value = [1, 2] 3906->3908 3909 entropy = 1.0 samples = 2 value = [1, 1] 3908->3909 3910 entropy = 0.0 samples = 1 value = [0, 1] 3908->3910 3912 hours-per-week <= 43.0 entropy = 1.0 samples = 4 value = [2, 2] 3911->3912 3917 hours-per-week <= 53.5 entropy = 0.536 samples = 49 value = [6, 43] 3911->3917 3913 entropy = 0.0 samples = 1 value = [0, 1] 3912->3913 3914 sex_Female <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 3912->3914 3915 entropy = 1.0 samples = 2 value = [1, 1] 3914->3915 3916 entropy = 0.0 samples = 1 value = [1, 0] 3914->3916 3918 age <= 47.5 entropy = 0.345 samples = 31 value = [2, 29] 3917->3918 3929 age <= 49.5 entropy = 0.764 samples = 18 value = [4, 14] 3917->3929 3919 entropy = 0.0 samples = 13 value = [0, 13] 3918->3919 3920 education <= 13.5 entropy = 0.503 samples = 18 value = [2, 16] 3918->3920 3921 hours-per-week <= 47.5 entropy = 0.619 samples = 13 value = [2, 11] 3920->3921 3928 entropy = 0.0 samples = 5 value = [0, 5] 3920->3928 3922 age <= 48.5 entropy = 0.503 samples = 9 value = [1, 8] 3921->3922 3925 age <= 48.5 entropy = 0.811 samples = 4 value = [1, 3] 3921->3925 3923 entropy = 0.0 samples = 6 value = [0, 6] 3922->3923 3924 entropy = 0.918 samples = 3 value = [1, 2] 3922->3924 3926 entropy = 1.0 samples = 2 value = [1, 1] 3925->3926 3927 entropy = 0.0 samples = 2 value = [0, 2] 3925->3927 3930 age <= 46.5 entropy = 0.696 samples = 16 value = [3, 13] 3929->3930 3941 entropy = 1.0 samples = 2 value = [1, 1] 3929->3941 3931 education <= 13.5 entropy = 0.845 samples = 11 value = [3, 8] 3930->3931 3940 entropy = 0.0 samples = 5 value = [0, 5] 3930->3940 3932 hours-per-week <= 57.5 entropy = 0.811 samples = 8 value = [2, 6] 3931->3932 3937 hours-per-week <= 57.5 entropy = 0.918 samples = 3 value = [1, 2] 3931->3937 3933 age <= 45.5 entropy = 0.971 samples = 5 value = [2, 3] 3932->3933 3936 entropy = 0.0 samples = 3 value = [0, 3] 3932->3936 3934 entropy = 1.0 samples = 4 value = [2, 2] 3933->3934 3935 entropy = 0.0 samples = 1 value = [0, 1] 3933->3935 3938 entropy = 0.0 samples = 2 value = [0, 2] 3937->3938 3939 entropy = 0.0 samples = 1 value = [1, 0] 3937->3939 3944 entropy = 0.0 samples = 11 value = [0, 11] 3943->3944 3945 hours-per-week <= 62.5 entropy = 0.679 samples = 39 value = [7, 32] 3943->3945 3946 age <= 55.5 entropy = 0.722 samples = 35 value = [7, 28] 3945->3946 3971 entropy = 0.0 samples = 4 value = [0, 4] 3945->3971 3947 age <= 54.5 entropy = 0.559 samples = 23 value = [3, 20] 3946->3947 3960 age <= 60.0 entropy = 0.918 samples = 12 value = [4, 8] 3946->3960 3948 age <= 53.5 entropy = 0.65 samples = 18 value = [3, 15] 3947->3948 3959 entropy = 0.0 samples = 5 value = [0, 5] 3947->3959 3949 hours-per-week <= 52.5 entropy = 0.523 samples = 17 value = [2, 15] 3948->3949 3958 entropy = 0.0 samples = 1 value = [1, 0] 3948->3958 3950 education <= 13.5 entropy = 0.764 samples = 9 value = [2, 7] 3949->3950 3957 entropy = 0.0 samples = 8 value = [0, 8] 3949->3957 3951 age <= 52.5 entropy = 0.918 samples = 6 value = [2, 4] 3950->3951 3956 entropy = 0.0 samples = 3 value = [0, 3] 3950->3956 3952 age <= 51.5 entropy = 0.971 samples = 5 value = [2, 3] 3951->3952 3955 entropy = 0.0 samples = 1 value = [0, 1] 3951->3955 3953 entropy = 0.918 samples = 3 value = [1, 2] 3952->3953 3954 entropy = 1.0 samples = 2 value = [1, 1] 3952->3954 3961 education <= 13.5 entropy = 1.0 samples = 6 value = [3, 3] 3960->3961 3968 age <= 64.0 entropy = 0.65 samples = 6 value = [1, 5] 3960->3968 3962 age <= 57.5 entropy = 0.918 samples = 3 value = [1, 2] 3961->3962 3965 age <= 58.0 entropy = 0.918 samples = 3 value = [2, 1] 3961->3965 3963 entropy = 1.0 samples = 2 value = [1, 1] 3962->3963 3964 entropy = 0.0 samples = 1 value = [0, 1] 3962->3964 3966 entropy = 0.0 samples = 1 value = [0, 1] 3965->3966 3967 entropy = 0.0 samples = 2 value = [2, 0] 3965->3967 3969 entropy = 0.0 samples = 4 value = [0, 4] 3968->3969 3970 entropy = 1.0 samples = 2 value = [1, 1] 3968->3970 3974 entropy = 0.0 samples = 1 value = [0, 1] 3973->3974 3975 entropy = 0.0 samples = 4 value = [4, 0] 3973->3975 3977 age <= 38.725 entropy = 0.248 samples = 97 value = [4, 93] 3976->3977 3998 education <= 15.5 entropy = 0.722 samples = 15 value = [3, 12] 3976->3998 3978 age <= 35.5 entropy = 0.61 samples = 20 value = [3, 17] 3977->3978 3991 education <= 15.5 entropy = 0.1 samples = 77 value = [1, 76] 3977->3991 3979 entropy = 0.0 samples = 7 value = [0, 7] 3978->3979 3980 age <= 36.5 entropy = 0.779 samples = 13 value = [3, 10] 3978->3980 3981 entropy = 0.0 samples = 1 value = [1, 0] 3980->3981 3982 hours-per-week <= 65.0 entropy = 0.65 samples = 12 value = [2, 10] 3980->3982 3983 hours-per-week <= 55.0 entropy = 0.811 samples = 8 value = [2, 6] 3982->3983 3990 entropy = 0.0 samples = 4 value = [0, 4] 3982->3990 3984 age <= 38.225 entropy = 0.65 samples = 6 value = [1, 5] 3983->3984 3989 entropy = 1.0 samples = 2 value = [1, 1] 3983->3989 3985 entropy = 0.0 samples = 3 value = [0, 3] 3984->3985 3986 workclass_Private <= 0.5 entropy = 0.918 samples = 3 value = [1, 2] 3984->3986 3987 entropy = 1.0 samples = 2 value = [1, 1] 3986->3987 3988 entropy = 0.0 samples = 1 value = [0, 1] 3986->3988 3992 entropy = 0.0 samples = 48 value = [0, 48] 3991->3992 3993 workclass_Self-emp <= 0.5 entropy = 0.216 samples = 29 value = [1, 28] 3991->3993 3994 entropy = 0.0 samples = 22 value = [0, 22] 3993->3994 3995 hours-per-week <= 55.0 entropy = 0.592 samples = 7 value = [1, 6] 3993->3995 3996 entropy = 1.0 samples = 2 value = [1, 1] 3995->3996 3997 entropy = 0.0 samples = 5 value = [0, 5] 3995->3997 3999 age <= 66.5 entropy = 0.881 samples = 10 value = [3, 7] 3998->3999 4006 entropy = 0.0 samples = 5 value = [0, 5] 3998->4006 4000 workclass_Private <= 0.5 entropy = 0.954 samples = 8 value = [3, 5] 3999->4000 4005 entropy = 0.0 samples = 2 value = [0, 2] 3999->4005 4001 age <= 64.5 entropy = 0.65 samples = 6 value = [1, 5] 4000->4001 4004 entropy = 0.0 samples = 2 value = [2, 0] 4000->4004 4002 entropy = 0.0 samples = 5 value = [0, 5] 4001->4002 4003 entropy = 0.0 samples = 1 value = [1, 0] 4001->4003
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